Sandahl Nelson
Practice Test 2013 AP Statistics
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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AP Statistics Practice Test 2013 AP Statistics
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Post by Ermelindo Furtado Varela on September 18, 2023
Why do I keep getting this error? I am already using Chrome and able to watch all the other videos, except for those related to practice exams.
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Fri Aug 19, 2022 6:32 PM
Post by Serena Ge on June 9, 2021
It says "Please visit Educator.com using the Chrome browser to play the video" but I'm already on chrome. It's just like this section 8 though. Doesn't work on neither mac nor pc
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Post by Firebird wang on October 5, 2016
Im sorry, may I ask why does the Practice Test 2013 AP Statistics can't be opened? It shows Network failure: retrying