Sandahl Nelson
Hypothesis Testing
Slide Duration:Table of Contents
Section 1: Describing Data: Graphically & Numerically
Constructing & Interpreting Graphs
37m 14s
- Intro0:00
- Objectives0:08
- Categorical Data0:26
- Pie Charts0:27
- Bar Graphs1:20
- (More) Bar Graphs2:25
- Comparative2:26
- Relative Frequency3:30
- Numerical Data: Discrete4:35
- Dot Plots4:36
- Stem and Leaf Plots6:08
- Example: Stem Plot7:55
- Example: Stem Plot7:56
- Numerical Data: Continuous9:03
- Numerical Data (Continuous)9:04
- Example I: Histogram10:57
- Numerical Data: Cumulative Frequency Plots16:49
- Frequency Polygon16:50
- Ogive Plot18:00
- Describe the Distribution19:42
- SOCS: Shape, Outlier, Center, Spread19:43
- Shape20:28
- Unimodal, Bimodal, or Multimodal20:29
- Symmetric Distribution21:48
- Positively Skewed Distribution21:30
- Negatively Skewed Distribution21:46
- Example II: Describe the Distribution22:06
- Stem Plots to Compare Two Groups of Data23:06
- Stem Plots to Compare Two Groups of Data23:06
- Example III: Compare the Distribution23:47
- Example IV: Describe the Distribution of Quiz Scores27:45
- Example V: Stem Plot29:26
- Example VI: Bar Graph & Relative Frequency30:53
Summarizing Distributions of Univariate Data
1h 7m 37s
- Intro0:00
- Objectives0:10
- Measuring Center0:42
- Median0:43
- Mean0:56
- Example: Find the Median and Mean1:59
- Measuring Position6:59
- Percentiles7:10
- Quartiles7:39
- Example: Find the Quartiles8:58
- Measuring Spread11:13
- Range11:14
- IQR11:33
- Variance11:55
- Example: Measuring Spread13:21
- Example: Find the Measures of Spread22:09
- Outliers27:23
- Outliers27:24
- Example: Outliers29:05
- Boxplots31:44
- 5-number Summary31:45
- Example I: Boxplot33:55
- Describe the Distribution44:20
- SOCS: Shape, Outlier, Center, Spread44:21
- Choosing Your Measure of Center & Spread45:16
- Example II: Describe the Distribution46:08
- The Effect of Changing Units on Summary Measures48:26
- Linear Transformations48:27
- Example: Distribution of Ages50:42
- Example III: Modified Boxplot & Describe the Distribution53:26
- Example IV: Describe the Distribution1:02:37
Section 2: Correlation & Regression
Correlation & Regression
50m 16s
- Intro0:00
- Objectives0:07
- Scatterplots0:30
- Scatterplots0:31
- Interpreting Scatterplots2:20
- Direction2:34
- Form2:50
- Strength3:29
- Example: Describe the Direction, Form, and Strength of the Scatterplot4:00
- Correlation Coefficient ( r )5:22
- Correlation Coefficient ( r )5:23
- Example: Correlation Coefficient ( r )7:52
- Approximate the Correlation Coefficient7:53
- Interpret the Correlation Coefficient8:48
- Least Squares Regression Line (LSRL)9:23
- Least Squares Regression Line (LSRL)9:24
- Interpreting the LSRL10:45
- y-intercept, Slope, Mean, and SD10:46
- Example: Interpreting the LSRL14:48
- Step 1: Determine the Least-squares Regression Line14:49
- Step 2: Interpret the Slope and y-intercept of the Regression Line18:28
- Step 3: Interpret the Correlation20:56
- Coefficient of Determination23:50
- R² = (r)²23:51
- Residuals26:04
- Residual = Observed y - Predicted y26:05
- Residual Plot27:04
- Example: Calculate the Residual28:33
- Example: Draw the Residual Plot31:18
- Example I: Explanatory Variable & Response Variable37:47
- Example II: Find the Least-squares Regression Line39:08
- Example III: Calculate the Residual44:10
- Example IV: Predicted Value47:50
- Example V: Residual Value49:28
Regression, Part II
23m 26s
- Intro0:00
- Objectives0:10
- Outliers and Influential Points0:20
- An OUTLIER0:21
- Influential Observations1:05
- Transformations to Achieve Linearity2:39
- Transformations to Achieve Linearity: When We Need It2:40
- Transformations to Achieve Linearity: How We Use It4:41
- Example I: Expected Number of Sales7:11
- Confounding11:13
- Confounding11:14
- Correlation Does NOT Prove Causation11:55
- Correlation Does NOT Prove Causation11:56
- Lurking Variables13:06
- Lurking Variables & Common Response13:07
- Confounding14:25
- Confounding14:26
- Example: Promotion to Increase Movie Sales15:11
- Example II: Causation, Confounding, or Common Response16:26
- Example III: Correlation18:25
- Example IV: Confounding & Common Response19:50
Section 3: Surveys & Experiments
Planning & Conducting Surveys
29m 35s
- Intro0:00
- Objectives0:09
- Census vs. Survey, Parameter vs. Statistics0:28
- Census vs. Survey, Parameter vs. Statistics0:29
- Characteristics of a Well-Designed and Well-Conducted Survey2:15
- Representative Sample2:16
- Random Sample3:38
- Does Not Introduce Bias4:02
- Bias4:16
- What Is It?4:17
- How Might It Occur?5:26
- Example I: Identify the Type of Bias7:03
- Random Sampling10:25
- Simple Random Sample (SRS)10:26
- Example II: Random Sampling13:26
- Random Sampling, Cont.16:44
- Stratified Random Sampling16:55
- Cluster Sample18:06
- Systematic Random Sample19:16
- Example III: Random Sampling20:52
- Non-Random Sampling22:28
- Convenience Sample22:29
- Voluntary Response Sample22:54
- Example IV: Sampling Design25:01
- Specify The Population25:02
- Describe The Sampling Design. Will You Use a Stratified Sample?26:46
Planning & Conducting Experiments
41m 31s
- Intro0:00
- Objectives0:09
- Experiments vs. Observational Studies0:44
- Observational Study0:45
- Experiment1:28
- Example I: Experimental or Observational?2:09
- Example II: Experimental or Observational?2:57
- Placebo Effect3:51
- Placebo Effect3:52
- Characteristics of a Well-designed and Well-conducted Experiment4:42
- Control4:43
- Replicate5:32
- Randomize6:32
- Example III: Control Groups7:33
- Completely Randomized Design9:01
- Completely Randomized Design9:02
- Outline/Map of Completely Randomized Design9:55
- Outline/Map of Completely Randomized Design9:56
- Example IV: Completely Randomized Design11:35
- Block Randomization14:23
- Block Randomization14:24
- Randomized Block Design15:29
- Randomized Block Design15:30
- Example V: Randomized Block Design18:06
- Matched Pairs Design21:08
- Matched Pairs Design21:09
- Example V: Types of Experiments22:42
- Example VI: Types of Experiments24:17
- Example VII: Types of Experiments26:24
- Experimental Set Up28:28
- Treatment28:29
- Experimental Units29:13
- Response29:32
- Double-blind Experiment31:06
- Double-blind Experiment31:07
- Example VIII: Double-blind Experiment32:37
- Example IX: Design a Study to Test Hypothesis37:04
- Generalizability of Results40:39
- Statistically Significant Data40:40
Section 4: Probability & Expected Value
Probability Overview
1h 22m 17s
- Intro0:00
- Objectives0:21
- Interpreting Probability0:46
- Probability of a Random Outcome or the Long Term Relative Frequency0:47
- Law of Large Numbers1:42
- Expected Value1:43
- Example I: Probability in Poker2:21
- Probability Model4:31
- Sample Space (S)4:32
- Event5:15
- Probabilities6:03
- Example II: Basketball Free Throws6:37
- Part 1: Sample Space6:46
- Part 2: Event8:08
- Part 3: Probability8:48
- Disjoin Events (aka Mutually Exclusive)11:00
- Disjoin Events (aka Mutually Exclusive)11:01
- Example III: Advertising Contracts12:23
- Part A: Venn Diagram12:24
- Probability of Disjoin Events14:03
- Probability of Disjoin Events14:04
- Example IV: Probability of Disjoin Events15:58
- Independence vs. Dependence18:11
- Independence vs. Dependence18:12
- Example V: Independence vs. Dependence20:26
- Example VI: Independence vs. Dependence22:23
- Probability Rules23:13
- Probability Rules23:14
- Probability Notation23:31
- P (A or B)23:32
- P (A and B)23:58
- P ( A given B happened)24:24
- P ( not A)24:44
- Example VII: Probability Notation25:17
- Probability Rule Notation26:49
- A or B26:50
- A and B27:40
- Example VIII: Determine if These Two Events are Independent29:05
- Example IX: Conditional Probability of Wining31:39
- Example X: Conditional Probability of Students36:46
- Part A: Probability36:47
- Part B: Conditional Probability38:18
- Part C: Conditional Probability39:59
- Example XI: Conditional Probability of Children42:53
- Part A: All Boys42:54
- Part B: All Girls44:44
- Part C: Exactly Two Boys or Exactly Two Girls45:50
- Part D: At Least One Child of Each Sex50:18
- Overview52:52
- Complement52:53
- Mutually Exclusive53:30
- Intersection53:49
- Union54:44
- Independent55:34
- Bayes Rule56:02
- Bayes Rule56:03
- Example XI: Probability & Bayes Rule59:43
- Example XII: Probability & Bayes Rule1:07:49
- Simulations1:05:46
- Simulations1:05:47
- Example XIII: Simulations1:07:10
Intro to Probability for Discrete Random Variables
31m 37s
- Intro0:00
- Objectives0:09
- Discrete vs. Continuous Random Variables0:29
- Discrete Random Variables0:30
- Continuous Random Variables1:12
- Probability Distribution3:36
- Probability Distribution for a Discrete Random Variables3:37
- Probability Rules4:20
- Example I: Find the Probability4:51
- Example II: Construct a Probability Distribution6:15
- Mean9:35
- Expected Value9:36
- Example: Expected Number of Customers10:08
- Variance13:19
- Variance13:20
- Example: Variance14:34
- Example III: Probability Analysis18:01
- Example IV: Expected Profit25:25
Discrete Random Variables
39m 6s
- Intro0:00
- Objectives0:08
- Binomial Distribution0:14
- BINP0:15
- B0:34
- I0:49
- N1:00
- P1:20
- Example I: Binomial Distribution1:43
- Question 1: Is a Binomial Distribution a Reasonable Probability Model for the Random Variable X?1:44
- Question 2: Is a Binomial Distribution a Reasonable Probability Model for the Random Variable X?3:43
- Binomial Probability5:11
- Binompdf (n, p, x)5:12
- Example II: Determine the Probability10:37
- Part A: Determine the Probability that Exactly One of the Toasters is Defective10:38
- Part B: Determine the Probability that At Most Two of the Toasters are Defective16:40
- Part C: Determine the Probability that More Than Three of the Toasters are Defective21:42
- Geometric Distribution24:11
- Geometric Distribution24:12
- Example III: Geometric Distribution & Probability25:14
- Part A: Geometric Distribution25:15
- Geometric Probability26:55
- Geometpdf (p, x)26:56
- Example III: Geometric Distribution & Probability27:50
- Part B: Geometric Probability of Exactly Four Patients27:51
- Part C: Geometric Probability of At Most Five Patients31:19
- Mean and SDs33:47
- Binomial33:48
- Geometric34:28
- Example IV: Defective Units34:53
- Example V: Number of Patients35:58
Combining Independent Random Variables
18m 56s
- Intro0:00
- Objectives0:09
- Mean and Standard Deviation of Two Random Variables0:26
- Mean and Standard Deviation of Two Random Variables0:27
- Example I: Average and Standard Deviation1:58
- Example II: Average and Standard Deviation4:37
- Transforming Random Variables: “Linear Transformations”6:10
- Transforming Random Variables: “Linear Transformations”6:11
- Example III: Mean and Standard Deviation7:02
- Example IV: Mean and Standard Deviation10:23
- Example V: Mean and Standard Deviation14:14
- Part 1: Mean & SD14:15
- Part 2: Mean & SD16:30
Normal Random Variables
59m 34s
- Intro0:00
- Objectives0:08
- The Empirical Rule0:28
- 68%0:29
- 95%1:43
- 99.70%2:00
- The Empirical Rule, Cont.2:31
- The Empirical Rule, Cont.2:32
- Example I: The Empirical Rule3:24
- Z-Score8:17
- Z-Score8:18
- Example II: Z-Score10:08
- Using the Normal Table13:03
- Using the Normal Table13:04
- Using the Normal Table, Cont.15:05
- Example III: Using the Normal Table and Z-score to Calculate Probability16:01
- Step 1: Sketch16:02
- Step 2: Calculate Z-score18:16
- Step 3: Solve for Probability Using the Normal Table19:14
- Example IV: Using the Normal Table and Z-score to Calculate Probability20:29
- Step 1: Sketch20:30
- Step 2: Calculate Z-score21:52
- Step 3: Solve for Probability Using the Normal Table22:36
- Example V: Using the Normal Table and Z-score to Calculate Probability27:20
- Step 1: Sketch27:42
- Step 2: Calculate Z-score28:14
- Step 3: Solve for Probability Using the Normal Table29:45
- Example VI: Using the Normal Table and Z-score to Calculate Probability34:00
- Step 1: Sketch34:01
- Step 2: Calculate Z-score35:48
- Step 3: Solve for Probability Using the Normal Table36:56
- Example VII: Using the Normal Table and Z-score to Calculate Probability41:21
- Step 1: Sketch41:22
- Step 2: Calculate Z-score44:15
- Step 3: Solve for Probability Using the Normal Table47:26
- Example VIII: Calculate the Standard Deviation of the Random Normal Variable49:54
- Step 1: Sketch49:55
- Step 2: Calculate Z-score51:16
- Step 3: Solve for Standard Deviation53:16
- Example VIII: Calculate the Mean of the Distribution55:11
- Step 1: Sketch55:12
- Step 2: Calculate Z-score56:36
- Step 3: Solve for Mean57:42
Section 6: Distribution of Data
Sampling Distributions
38m 27s
- Intro0:00
- Objectives0:07
- Parameter vs. Statistics0:25
- Parameter vs. Statistics0:26
- Sampling Distribution2:03
- Sampling Distribution2:04
- Central Limit Theorem3:15
- Central Limit Theorem3:16
- Central Limit Theorem, Cont.7:23
- Example I: Sampling Distribution Graph9:20
- Conditions (RIN)11:12
- Random11:13
- Independent12:04
- Normal13:40
- Sampling Distribution of a Sample Mean15:19
- Sampling Distribution of a Sample Mean15:20
- Example II: Calculate the Mean and SD of a Sampling Distribution17:17
- Sampling Distribution of a Sample Proportion21:07
- Sampling Distribution of a Sample Proportion21:08
- Example III: Mean, SD, Sample Size, and Probability of a Sampling Distribution22:29
- Part A: Calculate the Mean and SD of a Sampling Distribution22:30
- Part B: Sample Size26:18
- Part C: Probability29:30
- Example IV: Probability of a Sampling Distribution33:40
- Part A: Probability of a Random Selection33:41
- Part B: Probability of the Mean35:46
Section 7: Statistical Inference
Confidence Intervals
56m 37s
- Intro0:00
- Lesson Overview0:07
- Why Calculate a Confidence Interval?0:28
- Using a Statistic to Estimate a Parameter0:29
- What is a Confidence Interval?1:24
- Confidence Interval1:25
- General math Behind a Confidence Interval2:51
- Point Estimate2:52
- Critical Value4:34
- Z-Table6:06
- Z-Table6:07
- T-Table7:07
- T-Table7:08
- General math Behind a Confidence Interval7:50
- Point Estimate7:51
- Critical Value: Mean & Proportion8:00
- Standard Error: Mean & Proportion8:15
- Calculating Using Your Calculator10:46
- Steps to Calculating a Confidence Interval12:09
- Step 1: Read12:10
- Step 2: Check Your Conditions12:58
- Step 3: Calculate15:33
- Step 4: Interpret16:12
- Example I: Confidence Interval16:29
- Example II: Confidence Interval29:57
- Example III: Confidence Interval42:31
Hypothesis Testing
1h 12m 16s
- Intro0:00
- Lesson Overview0:07
- Why do a Hypothesis Test?0:29
- Using a Statistic to Test a Claim about a Parameter0:30
- Steps for Calculating a Hypothesis Test1:13
- 1. Write the Hypothesis1:14
- 2. Check Conditions1:30
- 3. Calculate the Test Statistic1:34
- 4. Look Up the P-value & Interpret1:49
- 5. Interpret1:50
- Example I: Hypothesis Testing Step by Step2:57
- 1. Write the Hypothesis5:04
- 2. Check Conditions8:43
- 3. Calculate the Test Statistic21:54
- 4. Look Up the P-value20:07
- 5. Interpret23:45
- Example II: Hypothesis Testing Step by Step28:49
- 1. Write the Hypothesis28:50
- 2. Check Conditions32:00
- 3. Calculate the Test Statistic34:20
- 4. Look Up the P-value38:26
- 5. Interpret40:49
- Example III: Hypothesis Test for a Mean44:53
- Example IV: Hypothesis Test for a Proportion57:26
The T Distribution
41m 40s
- Intro0:00
- Lesson Overview0:07
- When Do We Use the T Distribution0:26
- When Do We Use the T Distribution0:27
- What is the T Distribution?1:46
- What is the T Distribution?1:47
- Confidence Interval Example2:49
- Construct and Interpret a 90% Confidence Interval to Estimate the Mean2:50
- Hypothesis Test Example16:59
- 1. Write the Hypothesis17:00
- 2. Check Conditions20:01
- 3. Calculate the Test Statistic21:24
- 4. Look Up the P-value24:39
- 5. Interpret27:23
- Matched Pairs T-test29:34
- Matched Pairs T-test29:35
- 1. Write the Hypothesis33:05
- 2. Check Conditions34:58
- 3. Calculate the Test Statistic35:52
- 4. Look Up the P-value38:12
- 5. Interpret39:28
Two Samples
1h 27m 23s
- Intro0:00
- Lesson Overview0:09
- What Will a 2 Sample Problem Look Like?0:40
- Example 10:41
- Example 22:01
- Writing Your Hypothesis3:36
- Writing Your Hypothesis3:37
- Hypothesis Test Example I7:02
- 1. Write the Hypothesis7:03
- 2. Check Conditions10:04
- 3. Calculate the Test Statistic13:21
- 4. Look Up the P-value20:54
- 5. Interpret22:48
- Hypothesis Test Example II24:50
- 1. Write the Hypothesis24:51
- 2. Check Conditions28:34
- 3. Calculate the Test Statistic29:46
- 4. Look Up the P-value36:27
- 5. Interpret39:01
- Example I: Two Samples Hypothesis Testing42:11
- Example II: Two Samples Hypothesis Testing53:30
- “Pick Your Test” Map1:10:47
- “Pick Your Test” Map1:10:48
- Example III: Reliability Testing1:18:31
Hypothesis Testing of Least-Squares Regression Line
53m 49s
- Intro0:00
- Lesson Overview0:10
- Review of Least-squares Regression and Interpretation0:29
- Correlation Coefficient ( r )0:30
- Equation of the Least-squares Regression Line1:02
- Example2:45
- Part A: Least-squares Regression Line2:46
- Part B: Slope of the Least-squares Regression Line6:03
- Test for the Regression Line7:50
- Is There a Correlation?7:51
- Is the y-intercept = 0?9:56
- Conditions for Hypothesis Testing10:49
- Linearity11:27
- Constant Variability12:35
- Normality13:40
- Independence15:16
- Hypothesis Testing16:10
- Standard Deviation of the Residuals16:11
- Standard Error of Slope17:30
- Test Statistic18:45
- Confidence Interval19:36
- Example: Hypothesis Testing20:45
- Part A: Test the Hypothesis20:46
- Part B: 95% Confidence Interval of the Slope32:51
- Interpreting Computer Output35:40
- Interpreting Computer Output35:41
- Example I: Interpreting Computer Output38:46
- Part A: Least-squares Regression Equation38:47
- Part B: Standard Error40:01
- Part C: Slope of the Least-squares Regression Line41:21
- Part D: Null and Alternative Hypotheses42:08
- Part E: Value of Test Statistic43:09
- Part G: P-Value44:03
- Part H: Is Income Useful for Predicting the Cost of a Person’s Car?45:46
- Part I: Estimated Cost46:57
- Example II: Interpreting Computer Output47:48
Hypothesis Tests for Categorical Data (Chi-Squared Tests)
1h 12m 55s
- Intro0:00
- Lesson Overview0:11
- How Do We Know to Use a Chi-Squared Test?0:27
- Categorical Data0:28
- Chi-Squared Goodness of Fit Test1:50
- One Categorical Variable with Counts in Each Category1:51
- What We Have Seen2:17
- New Question Type2:56
- Example I: Chi-Squared Goodness of Fit Test4:02
- Chi-Squared Goodness of Fit Steps Overview4:03
- Step 1: Hypothesis5:54
- Step 2: Expected7:42
- Step 3: Conditions10:34
- Step 4: Calculate11:44
- Step 5: P-Value & Chi-Square Distribution Table17:03
- Example II: Chi-Squared Goodness of Fit Test22:04
- Step 1: Hypothesis22:05
- Step 2: Expected24:55
- Step 3: Calculate29:05
- Step 4: P-Value & Chi-Square Distribution Table33:18
- Chi-Squared Test of: Homogeneity or Independence/Association34:31
- Homogeneity34:32
- Independence/Association35:42
- Example III: Chi-Squared Test of: Homogeneity or Independence/Association37:55
- Step 1: Hypothesis37:56
- Step 2: Expected40:28
- Step 3: Conditions46:48
- Step 4: Calculate47:49
- Step 5: P-Value & Chi-Square Distribution Table49:30
- As a Test of Association52:53
- As a Test of Association52:54
- Example IV: Chi-Squared Test of: Homogeneity or Independence/Association55:05
- Step 1: Hypothesis, Expected, and Conditions55:06
- Step 2: Calculate59:45
- Step3: P-Value & Chi-Square Distribution Table1:01:51
- Example V: Chi-Squared Test of: Homogeneity or Independence/Association1:02:48
- Step 1: Hypothesis1:02:49
- Step 2: Expected and Conditions1:05:12
- Step 3: Calculate1:06:36
- Step 4: P-Value & Chi-Square Distribution Table1:10:50
Section 8: AP Practice Test
Practice Test 2013 AP Statistics
1h 2m 57s
- Intro0:00
- Question 10:23
- Question 1: Part A0:24
- Question 1: Part B2:10
- Question 26:16
- Question 2: Part A6:17
- Question 2: Part B10:22
- Question 2: Part C12:09
- Question 314:30
- Question 3: Part A14:31
- Question 3: Part B18:19
- Question 424:49
- Question 4: Part A24:50
- Question 537:27
- Question 5: Part A37:28
- Question 5: Part B42:32
- Question 651:15
- Question 6: Part A51:16
- Question 6: Part B55:17
Practice Test 2014 AP Statistics
1h 7s
- Intro0:00
- Question 10:32
- Question 29:46
- Question 2: Part A9:47
- Question 2: Part B12:28
- Question 2: Part C13:22
- Question 315:38
- Question 3: Part A15:39
- Question 3: Part B18:40
- Question 427:33
- Question 4: Part A27:34
- Question 4: Part B30:05
- Question 534:15
- Question 5: Part 134:16
- Question 5: Part 237:29
- Question 5: Part 339:50
- Question 5: Part 440:59
- Question 5: Part 544:09
- Question 645:30
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For more information, please see full course syllabus of AP Statistics
AP Statistics Hypothesis Testing
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1 answer
Last reply by: Sandahl Nelson
Wed Jan 17, 2018 12:37 PM
Post by Reem Alzaabi on December 10, 2017
Can you please explqin the Power of a Test?
0 answers
Post by Reem Alzaabi on December 10, 2017
amazing video
THANKS!!