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Self studying Harvard's Stat 110---has anyone managed to do this without any additional materials?

Hello everyone! I recently started self studying Joe Blitzstein's Stat 110 course . From what I can see from a couple of Quora posts, there is usually a lot of collaboration involved when solving problem sets. I haven't gotten into the course much, but so far the lectures are pretty easy to follow along. The text needs a bit more time (since it has more examples, and thus more thinking). Once I got to the strategic practice, however, things got a lot more harder and keep wondering if I need additional background material.

Has anyone self-studied this course without any external materials? If so, is there a good way of going about the course? Currently, I am going back and forth between all the materials and once I read the solutions I understand a little bit better. But when I got to the story proofs for the first unit, I wasn't able to generate a story without looking at the solution and nodding to myself thinking *Oh yeah, that sounds correct*

Also, is there a subreddit/discord/slack for non-Harvard students who discuss stuff for this course? Currently I am thinking of pushing forward despite my slow pace for two months, and reassess then to see if I should instead do the EdX course (which I am assuming is designed for self-study)

Thanks in advance.

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STAT 110: Introduction to Probability

Statistics twitter account, professor joe blitzstein uses twitter to share articles and other tidbits with his students in statistics 110.  .

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Strategic Practice Problems

In statistics 110, professor joe blitzstein provides students "strategic practice problems," arranged by topic which help then do the problem set.  .

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Statistics 110 - Introduction to Probability

Course Description

A comprehensive introduction to calculus based probability. Basics: sample space, conditional probability, Bayes Theorem. Univariate distributions: mass functions and density, expectation and variance, binomial, Poisson, normal, and gamma distributions. Multivariate distributions: joint and conditional distribution, independence, transformation, multivariate normal and related distributions. Limit laws: probability inequalities, law of large numbers, central limit theorem. Monte Carlo (simulation) methods. Markov chains: transition probability, stationary distribution and convergence.

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The final exam will be held in Science Center 222 on Wednesday, August 16th at 9:00.

This 3 hour exam will be comprehensive, though will be weighted a bit towards material covered after the midterm. You may bring 2 pages of notes to the exam and a calculator. As with earlier exams, any tables that might be needed will be supplied with the exam.

Exam Week Office Hours

Monday: 1:00 - 3:00 Tuesday: 2:00 - 4:00 or by appointment

Copyright 2006 by Mark E. Irwin

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Statistics 110 - Probability

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Lecture 1: Probability and Counting | Statistics 110. Lecture 2: Story Proofs, Axioms of Probability | Statistics 110. Lecture 3: Birthday Problem, Properties of Probability | Statistics 110. Lecture 4: Conditional Probability | Statistics 110. Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110. Lecture 6: Monty Hall, Simpson's Paradox | Statistics 110. Lecture 7: Gambler's Ruin and Random Variables | Statistics 110. Lecture 8: Random Variables and Their Distributions | Statistics 110. Lecture 9: Expectation, Indicator Random Variables, Linearity | Statistics 110. Lecture 10: Expectation Continued | Statistics 110. Lecture 11: The Poisson distribution | Statistics 110. Lecture 12: Discrete vs. Continuous, the Uniform | Statistics 110. Lecture 13: Normal distribution | Statistics 110. Lecture 14: Location, Scale, and LOTUS | Statistics 110. Lecture 15: Midterm Review | Statistics 110. Lecture 16: Exponential Distribution | Statistics 110. Lecture 17: Moment Generating Functions | Statistics 110. Lecture 18: MGFs Continued | Statistics 110. Lecture 19: Joint, Conditional, and Marginal Distributions | Statistics 110. Lecture 20: Multinomial and Cauchy | Statistics 110. Lecture 21: Covariance and Correlation | Statistics 110. Lecture 22: Transformations and Convolutions | Statistics 110. Lecture 23: Beta distribution | Statistics 110. Lecture 24: Gamma distribution and Poisson process | Statistics 110. Lecture 25: Order Statistics and Conditional Expectation | Statistics 110. Lecture 26: Conditional Expectation Continued | Statistics 110. Lecture 27: Conditional Expectation given an R.V. | Statistics 110. Lecture 28: Inequalities | Statistics 110. Lecture 29: Law of Large Numbers and Central Limit Theorem | Statistics 110. Lecture 30: Chi-Square, Student-t, Multivariate Normal | Statistics 110. Lecture 31: Markov Chains | Statistics 110. Lecture 32: Markov Chains Continued | Statistics 110. Lecture 33: Markov Chains Continued Further | Statistics 110. Lecture 34: A Look Ahead | Statistics 110. Joseph Blitzstein: "The Soul of Statistics" | Harvard Thinks Big 4.

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11.1 Facts About the Chi-Square Distribution

Decide whether the following statements are true or false.

As the number of degrees of freedom increases, the graph of the chi-square distribution looks more and more symmetrical.

The standard deviation of the chi-square distribution is twice the mean.

The mean and the median of the chi-square distribution are the same if df = 24.

11.2 Goodness-of-Fit Test

For each problem, use a solution sheet to solve the hypothesis test problem. Go to Appendix E Solution Sheets for the chi-square solution sheet. Round expected frequency to two decimal places.

A six-sided die is rolled 120 times. Fill in the expected frequency column. Then, conduct a hypothesis test to determine if the die is fair. The data in Table 11.34 are the result of the 120 rolls.

Face Value Frequency Expected Frequency
1 15
2 29
3 16
4 15
5 30
6 15

The marital status distribution of the U.S. male population, ages 15 and older, is as shown in Table 11.35 .

Marital Status % Expected Frequency
31.3%
56.1%
2.5%
10.1%

Suppose that a random sample of 400 U.S. males, 18 to 24 years old, yielded the following frequency distribution. We are interested in whether this age group of males fits the distribution of the U.S. adult population. Calculate the frequency one would expect when surveying 400 people. Fill in Table 11.35 , rounding to two decimal places.

Marital Status Frequency
140
238
2
20

Use the following information to answer the next two exercises. The columns in Table 11.37 contain the Race/Ethnicity of U.S. Public Schools for a recent year, the percentages for the Advanced Placement Examinee Population for that class, and the Overall Student Population. Suppose the right column contains the results of a survey of 1,000 local students from that year who took an AP exam.

Race/Ethnicity AP Examinee Population Overall Student Population Survey Frequency
10.2% 5.4% 113
8.2% 14.5% 94
15.5% 15.9% 136
0.6% 1.2% 10
59.4% 61.6% 604
6.1% 1.4% 43

Perform a goodness-of-fit test to determine whether the local results follow the distribution of the U.S. overall student population based on ethnicity.

Perform a goodness-of-fit test to determine whether the local results follow the distribution of U.S. AP examinee population, based on ethnicity.

The city of South Lake Tahoe, California, has an Asian population of 1,419 out of a total population of 23,609. Suppose that a survey of 1,419 self-reported Asians in the borough of Manhattan in the New York City area yielded the data in Table 11.38 . Conduct a goodness-of-fit test to determine if the self-reported subgroups of Asians in Manhattan fit that of the South Lake Tahoe area.

Race South Lake Tahoe Frequency Manhattan Frequency
131 174
118 557
1,045 518
80 54
12 29
9 21
24 66

Use the following information to answer the next two exercises. UCLA conducted a survey of more than 263,000 college freshmen from 385 colleges in fall 2005. The results of students’ expected majors by gender were reported in The Chronicle of Higher Education (2/2/2006) . Suppose a survey of 5,000 graduating females and 5,000 graduating males was done as a follow-up last year to determine what their actual majors were. The results are shown in the tables for Exercise 11.77 and Exercise 11.78 . The second column in each table does not add to 100 percent because of rounding.

Conduct a goodness-of-fit test to determine if the actual college majors of graduating females fit the distribution of their expected majors.

Major Females—Expected Major Females—Actual Major
14% 670
8.4% 410
13.1% 685
13% 650
2.6% 145
2.6% 125
18.9% 975
13% 605
0.4% 15
5.8% 300
8% 420

Conduct a goodness-of-fit test to determine if the actual college majors of graduating males fit the distribution of their expected majors.

Major Males—Expected Major Males—Actual Major
11% 600
6.7% 330
22.7% 1,130
5.8% 305
15.6% 800
3.6% 175
9.3% 460
7.6% 370
1.8% 90
8.2% 400
6.6% 340

Read the statement and decide whether it is true or false.

In a goodness-of-fit test, the expected values are the values we would expect if the null hypothesis were true.

In general, if the observed values and expected values of a goodness-of-fit test are not close together, then the test statistic can get very large and on a graph will be way out in the right tail.

Use a goodness-of-fit test to determine if high school principals believe that students are absent equally during the week.

The test to use to determine if a six-sided die is fair is a goodness-of-fit test.

In a goodness-of-fit test, if the p -value is 0.0113, in general, do not reject the null hypothesis.

A sample of 212 commercial businesses was surveyed for recycling one commodity; a commodity here means any one type of recyclable material such as plastic or aluminum. Table 11.41 shows the business categories in the survey, the sample size of each category, and the number of businesses in each category that recycle one commodity. Based on the study, on average half of the businesses were expected to be recycling one commodity. As a result, the last column shows the expected number of businesses in each category that recycle one commodity. At the 5 percent significance level, perform a hypothesis test to determine if the observed number of businesses that recycle one commodity follows the uniform distribution of the expected values.

Business Type Number in Class Observed Number that Recycle One Commodity Expected Number that Recycle One Commodity
35 19 17.5
48 27 24
53 35 26.5
52 21 26
24 9 12

Table 11.42 contains information from a survey of 499 participants classified according to their age groups. The second column shows the percentage of obese people per age class among the study participants. The last column comes from a different study at the national level that shows the corresponding percentages of obese people in the same age classes in the United States. Perform a hypothesis test at the 5 percent significance level to determine whether the survey participants are a representative sample of the USA obese population.

Age Class (years) Obese (Percentage) Expected USA Average (Percentage)
75 32.6
26.5 32.6
13.6 36.6
21.9 36.6
21 39.7

11.3 Test of Independence

For each problem, use a solution sheet to solve the hypothesis test problem. Go to Appendix E for the chi-square solution sheet. Round expected frequency to two decimal places.

A recent debate about where in the U.S. skiers believe the skiing is best prompted the following survey. Test to see if the best ski area is independent of the level of the skier.

U.S. Ski Area Beginner Intermediate Advanced
20 30 40
10 30 60
10 40 50

Car manufacturers are interested in whether there is a relationship between the size of car an individual drives and the number of people in the driver’s family—that is, whether car size and family size are independent. To test this, suppose that 800 car owners were randomly surveyed with the results in Table 11.44 . Conduct a test of independence.

Family Size Sub & Compact Mid-Size Full-Size Van & Truck
20 35 40 35
20 50 70 80
20 50 100 90
20 30 70 70

College students may be interested in whether their majors have any effect on starting salaries after graduation. Suppose that 300 recent graduates were surveyed as to their majors in college and their starting salaries after graduation. Table 11.45 shows the data. Conduct a test of independence.

Major < $50,000 $50,000–$68,999 $69,000 +
5 20 5
10 30 60
10 15 15
10 20 30
20 30 20

Some travel agents claim that honeymoon hotspots vary according to age of the bride. Suppose that 280 recent brides were interviewed as to where they spent their honeymoons. The information is given in Table 11.46 . Conduct a test of independence.

Location 20–29 30–39 40–49 50+
15 25 25 20
15 25 25 10
10 25 15 5
20 25 15 5

A manager of a sports club keeps information concerning the main sport in which members participate and their ages. To test whether there is a relationship between the age of a member and his or her choice of sport, 643 members of the sports club are randomly selected. Conduct a test of independence.

Sport 18–25 26–30 31–40 41+
42 58 30 46
58 76 38 65
72 60 65 33

A major food manufacturer is concerned that the sales for its skinny french fries have been decreasing. As a part of a feasibility study, the company conducts research into the types of fries sold across the country to determine if the type of fries sold is independent of the area of the country. The results of the study are shown in Table 11.48 . Conduct a test of independence.

Type of Fries Northeast South Central West
70 50 20 25
100 60 15 30
20 40 10 10

According to Dan Leonard, an independent insurance agent in the Buffalo, New York area, the following is a breakdown of the amount of life insurance purchased by males in the following age groups. He is interested in whether the age of the male and the amount of life insurance purchased are independent events. Conduct a test for independence.

Age of Males None < $200,000 $200,000–$400,000 $401,001–$1,000,000 $1,000,001+
40 15 40 0 5
35 5 20 20 10
20 0 30 0 30
40 30 15 15 10

Suppose that 600 thirty-year-olds were surveyed to determine whether there is a relationship between the level of education an individual has and salary. Conduct a test of independence.

Annual Salary Not a High School Graduate High School Graduate College Graduate Masters or Doctorate
15 25 10 5
20 40 70 30
10 20 40 55
5 10 20 60
0 5 10 150

The number of degrees of freedom for a test of independence is equal to the sample size minus one.

The test for independence uses tables of observed and expected data values.

The test to use when determining if the college or university a student chooses to attend is related to his or her socioeconomic status is a test for independence.

In a test of independence, the expected number is equal to the row total multiplied by the column total divided by the total surveyed.

An ice cream maker performs a nationwide survey about favorite flavors of ice cream in different geographic areas of the United States. Based on Table 11.51 , do the numbers suggest that geographic location is independent of favorite ice cream flavors? Test at the 5 percent significance level.

U.S. Region/Flavor Strawberry Chocolate Vanilla Rocky Road Mint Chocolate Chip Pistachio Row Total
12 21 22 19 15 8 97
10 32 22 11 15 6 96
8 31 27 8 15 7 96
15 28 30 8 15 6 102
45 112 101 46 60 27 391

Table 11.52 provides results of a recent survey of the youngest online entrepreneurs whose net worth is estimated at one million dollars or more. Their ages range from 17 to 30. Each cell in the table illustrates the number of entrepreneurs who correspond to the specific age group and their net worth. Are the ages and net worth independent? Perform a test of independence at the 5 percent significance level.

Age Group/Net Worth Value (in millions of U.S. dollars) 1–5 6–24 ≥25 Row Total
8 7 5 20
6 5 9 20
14 12 14 40

A 2013 poll in California surveyed people about a new tax. The results are presented in Table 11.53 and are classified by ethnic group and response type. Are the poll responses independent of the participants’ ethnic group? Conduct a test of independence at the 5 percent significance level.

Opinion/Ethnicity Asian American White/Non-Hispanic African American Latino Row Total
48 433 41 160 682
54 234 24 147 459
16 43 16 19 94
118 710 81 326 1,235

11.4 Test for Homogeneity

For each word problem, use a solution sheet to solve the hypothesis test problem. Go to Appendix E Solution Sheets for the chi-square solution sheet. Round expected frequency to two decimal places.

A psychologist is interested in testing whether there is a difference in the distribution of personality types for business majors and social science majors. The results of the study are shown in Table 11.54 . Conduct a test of homogeneity. Test at a 5 percent level of significance.

41 52 46 61 58
72 75 63 80 65

Do men and women select different breakfasts? The breakfasts ordered by randomly selected men and women at a popular breakfast place are shown in Table 11.55 . Conduct a test for homogeneity at a 5 percent level of significance. 

47 35 28 53
65 59 55 60

A fisherman is interested in whether the distribution of fish caught in Green Valley Lake is the same as the distribution of fish caught in Echo Lake. Of the 191 randomly selected fish caught in Green Valley Lake, 105 were rainbow trout, 27 were other trout, 35 were bass, and 24 were catfish. Of the 293 randomly selected fish caught in Echo Lake, 115 were rainbow trout, 58 were other trout, 67 were bass, and 53 were catfish. Perform a test for homogeneity at a 5 percent level of significance.

In 2007, the United States had 1.5 million homeschooled students, according to the U.S. National Center for Education Statistics. In Table 11.56 , you can see that parents decide to homeschool their children for different reasons, and some reasons are ranked by parents as more important than others. According to the survey results shown in the table, is the distribution of applicable reasons the same as the distribution of the most important reason? Provide your assessment at the 5 percent significance level. Did you expect the result you obtained?

Reasons for Homeschooling Applicable Reason (in thousands of respondents) Most Important Reason (in thousands of respondents) Row Total
1,321 309 1,630
1,096 258 1,354
1,257 540 1,797
315 55 370
984 99 1,083
485 216 701
5,458 1,477 6,935

When looking at energy consumption, we are often interested in detecting trends over time and how they correlate among different countries. The information in Table 11.57 shows the average energy use in units of kg of oil equivalent per capita in the United States and the joint European Union countries (EU) for the six-year period 2005 to 2010. Do the energy use values in these two areas come from the same distribution? Perform the analysis at the 5 percent significance level.

Year European Union United States Row Total
3,413 7,164 10,557
3,302 7,057 10,359
3,505 7,488 10,993
3,537 7,758 11,295
3,595 7,697 11,292
3,613 7,847 11,460
20,965 45,011 65,976

The Insurance Institute for Highway Safety collects safety information about all types of cars every year and publishes a report of top safety picks among all cars, makes, and models. Table 11.58 presents the number of top safety picks in six car categories for the two years 2009 and 2013. Analyze the table data to conclude whether the distribution of cars that earned the top safety picks safety award has remained the same between 2009 and 2013. Derive your results at the 5 percent significance level.

Year/Car Type Small Mid-Size Large Small SUV Mid-Size SUV Large SUV Row Total
12 22 10 10 27 6 87
31 30 19 11 29 4 124
43 52 29 21 56 10 211

11.5 Comparison of the Chi-Square Tests

Is there a difference between the distribution of community college statistics students and the distribution of university statistics students in what technology they use on their homework? Of some randomly selected community college students, 43 used a computer, 102 used a calculator with built-in statistics functions, and 65 used a table from the textbook. Of some randomly selected university students, 28 used a computer, 33 used a calculator with built-in statistics functions, and 40 used a table from the textbook. Conduct an appropriate hypothesis test using a 0.05 level of significance.

If df = 2, the chi-square distribution has a shape that reminds us of the exponential.

11.6 Test of a Single Variance

Use the following information to answer the next 12 exercises. Suppose an airline claims that its flights are consistently on time with an average delay of at most 15 minutes. It claims that the average delay is so consistent that the variance is no more than 150 minutes. Doubting the consistency part of the claim, a disgruntled traveler calculates the delays for his next 25 flights. The average delay for those 25 flights is 22 minutes with a standard deviation of 15 minutes.

Is the traveler disputing the claim about the average or about the variance?

A sample standard deviation of 15 minutes is the same as a sample variance of __________ minutes.

Is this a right-tailed, left-tailed, or two-tailed test?

H 0 : __________

df = ________

chi-square test statistic = ________

p -value = ________

Graph the situation. Label and scale the horizontal axis. Mark the mean and test statistic. Shade the p -value.

Let α = 0.05 Decision: ________ Conclusion (write out in a complete sentence): ________

How did you know to test the variance instead of the mean?

If an additional test were done on the claim of the average delay, which distribution would you use?

If an additional test were done on the claim of the average delay, but 45 flights were surveyed, which distribution would you use?

A plant manager is concerned her equipment may need recalibrating. It seems that the actual weight of the 15-ounce cereal boxes it fills has been fluctuating. The standard deviation should be at most 0.5 ounces. To determine if the machine needs to be recalibrated, 84 randomly selected boxes of cereal from the next day’s production were weighed. The standard deviation of the 84 boxes was 0.54. Does the machine need to be recalibrated?

Consumers may be interested in whether the cost of a particular calculator varies from store to store. Based on surveying 43 stores, which yielded a sample mean of $84 and a sample standard deviation of $12, test the claim that the standard deviation is greater than $15.

Isabella, an accomplished Bay-to-Breakers runner, claims that the standard deviation for her time to run the 7.5 mile race is at most 3 minutes. To test her claim, Isabella looks up five of her race times. They are 55 minutes, 61 minutes, 58 minutes, 63 minutes, and 57 minutes.

Airline companies are interested in the consistency of the number of babies on each flight so that they have adequate safety equipment. They are also interested in the variation of the number of babies. Suppose that an airline executive believes the average number of babies on flights is six with a variance of nine at most. The airline conducts a survey. The results of the 18 flights surveyed give a sample average of 6.4 with a sample standard deviation of 3.9. Conduct a hypothesis test of the airline executive’s belief.

The number of births per woman in China is 1.6, down from 5.91 in 1966. This fertility rate has been attributed to the law passed in 1979 restricting births to one per woman. Suppose that a group of students studied whether the standard deviation of births per woman was greater than 0.75. They asked 50 women across China the number of births they had. The results are shown in Table 11.59 . Does the students’ survey indicate that the standard deviation is greater than 0.75?

# of Births Frequency
0 5
1 30
2 10
3 5

According to an avid aquarist, the average number of fish in a 20-gallon tank is 10, with a standard deviation of two. His friend, also an aquarist, does not believe that the standard deviation is two. She counts the number of fish in 15 other 20-gallon tanks. Based on the results that follow, do you think that the standard deviation is different from two? Data: 11; 10; 9; 10; 10; 11; 11; 10; 12; 9; 7; 9; 11; 10; and 11.

The manager of Frenchies is concerned that patrons are not consistently receiving the same amount of French fries with each order. The chef claims that the standard deviation for a 10-ounce order of fries is at most 1.5 ounces, but the manager thinks that it may be higher. He randomly weighs 49 orders of fries, which yields a mean of 11 ounces and a standard deviation of 2 ounces.

You want to buy a specific computer. A sales representative of the manufacturer claims that retail stores sell this computer at an average price of $1,249 with a very narrow standard deviation of $25. You find a website that has a price comparison for the same computer at a series of stores as follows: $1,299; $1,229.99; $1,193.08; $1,279; $1,224.95; $1,229.99; $1,269.95; and $1,249. Can you argue that pricing has a larger standard deviation than claimed by the manufacturer? Use the 5 percent significance level. As a potential buyer, what would be the practical conclusion from your analysis?

A company packages apples by weight. One of the weight grades is Class A apples. Class A apples have a mean weight of 150 grams, and there is a maximum allowed weight tolerance of 5 percent above or below the mean for apples in the same consumer package. A batch of apples is selected to be included in a Class A apple package. Given the following apple weights of the batch, does the fruit comply with the Class A grade weight tolerance requirements? Conduct an appropriate hypothesis test.

(a) At the 5 percent significance level

(b) At the 1 percent significance level

Weights in selected apple batch (in grams): 158; 167; 149; 169; 164; 139; 154; 150; 157; 171; 152; 161; 141; 166; and 172.

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  • Publisher/website: OpenStax
  • Book title: Statistics
  • Publication date: Mar 27, 2020
  • Location: Houston, Texas
  • Book URL: https://openstax.org/books/statistics/pages/1-introduction
  • Section URL: https://openstax.org/books/statistics/pages/11-homework

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COMMENTS

  1. Strategic Practice and Homework Problems

    Actively solving practice problems is essential for learning probability. Strategic practice problems are organized by concept, to test and reinforce understanding of that concept. Homework problems usually do not say which concepts are involved, and often require combining several concepts. Each of the Strategic Practice documents here ...

  2. Statistics 110: Probability

    The edX course focuses on animations, interactive features, readings, and problem-solving, and is complementary to the Stat 110 lecture videos on YouTube, which are available at https://goo.gl/i7njSb. The Stat110x animations are available within the course and at https://goo.gl/g7pqTo

  3. Handouts

    Statistics 110: Probability. Joe Blitzstein, Professor of the Practice in Statistics Harvard University, Department of Statistics Contact

  4. Self studying Harvard's Stat 110---has anyone managed to do ...

    I really can't seem to figure out how to answer most of the word problems in the Stat 110, so I went with Morin in the meantime. I'm stuck on the first homework. I feel like I just need to practice a lot more, and Blitzstein says that's what we need to do, but I just feel like the explanations in the practice problem solutions aren't enough, at ...

  5. Statistics 110: Probability

    Statistics 110 (Probability) has been taught at Harvard University by Joe Blitzstein (Professor of the Practice in Statistics, Harvard University) each year ...

  6. Syllabus

    A comprehensive introduction to probability as a language and toolbox for understanding statistics, science, risk, and randomness. ... Each unit has both practice problems and homework problems. The practice problems are worth 30% of your grade and the homework problems are worth 70%. ... The students and teaching fellows of Stat 110; edX ...

  7. Statistics and Probability

    Unit 7: Probability. 0/1600 Mastery points. Basic theoretical probability Probability using sample spaces Basic set operations Experimental probability. Randomness, probability, and simulation Addition rule Multiplication rule for independent events Multiplication rule for dependent events Conditional probability and independence.

  8. STAT 110: Introduction to Probability

    In Statistics 110, Professor Joe Blitzstein provides students "strategic practice problems," arranged by topic which help then do the problem set. ... Homework (44) Apply Homework filter; Pair and Share (42) Apply Pair and Share filter; Case Study (31) Apply Case Study filter;

  9. statistics-110-probability-lectures-and-homework/homework ...

    This repository contains lecture notes and homework of Statistics 110: Probability by Joe Blitzstein. - rubelchowdhury20/statistics-110-probability-lectures-and-homework

  10. Statistics 110

    Stat 110 Home: Assignments: Lecture Materials: ... Calendar: Mark Irwin: Statistics 110 - Introduction to Probability . Instructor: Mark Irwin Office: Science Center 611 Phone: 617-495-5617 E-mail: [email protected] Lectures: Monday - Friday , 11:00 - 12:00, Science Center 109

  11. Statistics 110

    Lecture 4: Conditional Probability | Statistics 110. Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110. Lecture 6: Monty Hall, Simpson's Paradox | Statistics 110. Lecture 7: Gambler's Ruin and Random Variables | Statistics 110.

  12. PDF Stat 110 Strategic Practice 1, Fall 2011 1 Naive Definition of Probability

    Stat 110 Homework 1, Fall 2011 Prof. Joe Blitzstein (Department of Statistics, Harvard University) 1. A certain family has 6 children, consisting of 3 boys and 3 girls. Assuming that all birth orders are equally likely, what is the probability that the 3 eldest children are the 3 girls? 2.

  13. Ch. 11 Homework

    Statistics Homework. Close. Contents Contents. Highlights. Print. Table of contents. Preface; 1 Sampling and Data. ... 110. A sample standard deviation of 15 minutes is the same as a sample variance of _____ minutes. 111. Is this a right-tailed, left-tailed, or two-tailed test?

  14. HW8 stats110

    View Homework Help - HW8 stats110 from STATISTICS 110 at University of California, Irvine. STATISTICS 110, FALL 2015 Homework #8 Assigned Wed, November 25 Due Wed, December 2 For this assignment

  15. Statistics 110 Home page

    Office Hours: Monday 12:45 -- 1:45 pm in Conference Room top of stairs in Sequoia. Steven Roberts - Room 108, Sequoia Hall. Phone: 5-6162. Email: robertss@stat. Office Hours: Friday 1:00-2:00 pm. Mu Zhu - Room 242, Sequoia Hall. Phone: 5-5952. Email: mzhu@stat. Office Hours: Tuesday 2:00 -- 3:00 pm. Find me in the PC lab, 2nd floor of Sequoia ...

  16. Statistics 110 Home page

    Remember that mail addressed to stat-110 will be received by the entire class; for questions, e-mail the TAs directly! ... Graded homework will be left in the pigeonholes on the second floor of the Statistics department. LATE HOMEWORK will be penalized at 5% of the maximum score per day. Homework turned in more than 7 days late will not be ...

  17. Stat110 Section 1 Solutions.pdf

    View Stat110_Section_1_Solutions.pdf from STAT 110 at Boston University. Statistics 110: Introduction to Probability Section 1, Fall 2021 Counting and Story Proofs Ethan Wang. AI Homework Help. Expert Help. Study Resources. ... homework. HW 3.docx. Solutions Available. American Military University. MATH 302. HW_01 solution. Solutions Available ...

  18. Homework 3 Solutions.pdf

    UC Irvine, Statistics 110/201 Homework Assignment #3 Assigned October 16, 2018 Due October 25, 2018 Question1 Use the Sparrows dataset and build a simple linear regression model that predicts the weight (in grams) of the bird from the wing length (in millimeters).

  19. HW #4

    IMG 0065 - Homework #1 solved. Probability And Statistics In The Life Sciences 100% (5) 4. AMS 110 Homework. Probability And Statistics In The Life Sciences 100% (4) 6. AMS 110 Notes Exam I. Probability And Statistics In The Life Sciences 87% (15)

  20. About

    The lecture videos are available on iTunes U and YouTube. Stat 110 is an introduction to probability as a language and set of tools for understanding statistics, science, risk, and randomness. The ideas and methods are useful in statistics, science, engineering, economics, finance, and everyday life. Topics include the following.

  21. Extra Credit

    5. Homework 1 answer key AMS 110 2021 STONY BROOK. Probability And Statistics In The Life Sciences. Assignments. 100% (6) 4. IMG 0065 - Homework #1 solved. Probability And Statistics In The Life Sciences. Assignments.

  22. PDF stat110 Introduction to Probability

    Statistics 110: Probability Harvard iTunes U Course Professor: Joe Blitzstein, Professor of the Practice, Harvard Statistics Department ... Homework: Solving a lot of problems is an extremely important part of learning probability. To help with this, we provide about 250 practice problems, with detailed solutions. We highly

  23. MATH 110 : Introduction to Statistics

    MATH 110 - MODULE 1 1.1 Intro to Stats Each person is an element Each column is the variable (i.e. age, gender, state, children, etc) Each row is the observation f Qualitative data can be numerical or non-numerical Quantitative data is ONLY numerical Each. MATH 110. Portage Learning. 55 views.