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AP Statistics Online Prof. Sandahl Nelson, M.S.
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Level Advanced
18 Lessons (15hr : 04min)
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Audio: English

Join Prof. Sandahl Nelson in her time-saving AP Statistics course that combines clear explanations of concepts along with tons of live AP-level examples. Prof. Nelson also covers how to use popular TI graphing calculators for analysis in class and during the exam.

Table of Contents
Course Details
About Prof. Nelson

Section 1: Describing Data: Graphically & Numerically
	Constructing & Interpreting Graphs			37:14
		Intro		0:00
		Objectives		0:08
		Categorical Data		0:26
			Pie Charts	0:27
			Bar Graphs	1:20
		(More) Bar Graphs		2:25
			Comparative	2:26
			Relative Frequency	3:30
		Numerical Data: Discrete		4:35
			Dot Plots	4:36
			Stem and Leaf Plots	6:08
		Example: Stem Plot		7:55
			Example: Stem Plot	7:56
		Numerical Data: Continuous		9:03
			Numerical Data (Continuous)	9:04
		Example I: Histogram		10:57
		Numerical Data: Cumulative Frequency Plots		16:49
			Frequency Polygon	16:50
			Ogive Plot	18:00
		Describe the Distribution		19:42
			SOCS: Shape, Outlier, Center, Spread	19:43
		Shape		20:28
			Unimodal, Bimodal, or Multimodal	20:29
			Symmetric Distribution	21:48
			Positively Skewed Distribution	21:30
			Negatively Skewed Distribution	21:46
		Example II: Describe the Distribution		22:06
		Stem Plots to Compare Two Groups of Data		23:06
			Stem Plots to Compare Two Groups of Data	23:06
		Example III: Compare the Distribution		23:47
		Example IV: Describe the Distribution of Quiz Scores		27:45
		Example V: Stem Plot		29:26
		Example VI: Bar Graph & Relative Frequency		30:53
	Summarizing Distributions of Univariate Data			1:07:37
		Intro		0:00
		Objectives		0:10
		Measuring Center		0:42
			Median	0:43
			Mean	0:56
		Example: Find the Median and Mean		1:59
		Measuring Position		6:59
			Percentiles	7:10
			Quartiles	7:39
		Example: Find the Quartiles		8:58
		Measuring Spread		11:13
			Range	11:14
			IQR	11:33
			Variance	11:55
			Example: Measuring Spread	13:21
		Example: Find the Measures of Spread		22:09
		Outliers		27:23
			Outliers	27:24
		Example: Outliers		29:05
		Boxplots		31:44
			5-number Summary	31:45
		Example I: Boxplot		33:55
		Describe the Distribution		44:20
			SOCS: Shape, Outlier, Center, Spread	44:21
			Choosing Your Measure of Center & Spread	45:16
		Example II: Describe the Distribution		46:08
		The Effect of Changing Units on Summary Measures		48:26
			Linear Transformations	48:27
			Example: Distribution of Ages	50:42
		Example III: Modified Boxplot & Describe the Distribution		53:26
		Example IV: Describe the Distribution		62:37
Section 2: Correlation & Regression
	Correlation & Regression			50:16
		Intro		0:00
		Objectives		0:07
		Scatterplots		0:30
			Scatterplots	0:31
		Interpreting Scatterplots		2:20
			Direction	2:34
			Form	2:50
			Strength	3:29
		Example: Describe the Direction, Form, and Strength of the Scatterplot		4:00
		Correlation Coefficient ( r )		5:22
			Correlation Coefficient ( r )	5:23
		Example: Correlation Coefficient ( r )		7:52
			Approximate the Correlation Coefficient	7:53
			Interpret the Correlation Coefficient	8:48
		Least Squares Regression Line (LSRL)		9:23
			Least Squares Regression Line (LSRL)	9:24
		Interpreting the LSRL		10:45
			y-intercept, Slope, Mean, and SD	10:46
		Example: Interpreting the LSRL		14:48
			Step 1: Determine the Least-squares Regression Line	14:49
			Step 2: Interpret the Slope and y-intercept of the Regression Line	18:28
			Step 3: Interpret the Correlation	20:56
		Coefficient of Determination		23:50
			R² = (r)²	23:51
		Residuals		26:04
			Residual = Observed y - Predicted y	26:05
			Residual Plot	27:04
		Example: Calculate the Residual		28:33
		Example: Draw the Residual Plot		31:18
		Example I: Explanatory Variable & Response Variable		37:47
		Example II: Find the Least-squares Regression Line		39:08
		Example III: Calculate the Residual		44:10
		Example IV: Predicted Value		47:50
		Example V: Residual Value		49:28
	Regression, Part II			23:26
		Intro		0:00
		Objectives		0:10
		Outliers and Influential Points		0:20
			An OUTLIER	0:21
			Influential Observations	1:05
		Transformations to Achieve Linearity		2:39
			Transformations to Achieve Linearity: When We Need It	2:40
			Transformations to Achieve Linearity: How We Use It	4:41
		Example I: Expected Number of Sales		7:11
		Confounding		11:13
			Confounding	11:14
		Correlation Does NOT Prove Causation		11:55
			Correlation Does NOT Prove Causation	11:56
		Lurking Variables		13:06
			Lurking Variables & Common Response	13:07
		Confounding		14:25
			Confounding	14:26
			Example: Promotion to Increase Movie Sales	15:11
		Example II: Causation, Confounding, or Common Response		16:26
		Example III: Correlation		18:25
		Example IV: Confounding & Common Response		19:50
Section 3: Surveys & Experiments
	Planning & Conducting Surveys			29:35
		Intro		0:00
		Objectives		0:09
		Census vs. Survey, Parameter vs. Statistics		0:28
			Census vs. Survey, Parameter vs. Statistics	0:29
		Characteristics of a Well-Designed and Well-Conducted Survey		2:15
			Representative Sample	2:16
			Random Sample	3:38
			Does Not Introduce Bias	4:02
		Bias		4:16
			What Is It?	4:17
			How Might It Occur?	5:26
		Example I: Identify the Type of Bias		7:03
		Random Sampling		10:25
			Simple Random Sample (SRS)	10:26
		Example II: Random Sampling		13:26
		Random Sampling, Cont.		16:44
			Stratified Random Sampling	16:55
			Cluster Sample	18:06
			Systematic Random Sample	19:16
		Example III: Random Sampling		20:52
		Non-Random Sampling		22:28
			Convenience Sample	22:29
			Voluntary Response Sample	22:54
		Example IV: Sampling Design		25:01
			Specify The Population	25:02
			Describe The Sampling Design. Will You Use a Stratified Sample?	26:46
	Planning & Conducting Experiments			41:31
		Intro		0:00
		Objectives		0:09
		Experiments vs. Observational Studies		0:44
			Observational Study	0:45
			Experiment	1:28
		Example I: Experimental or Observational?		2:09
		Example II: Experimental or Observational?		2:57
		Placebo Effect		3:51
			Placebo Effect	3:52
		Characteristics of a Well-designed and Well-conducted Experiment		4:42
			Control	4:43
			Replicate	5:32
			Randomize	6:32
		Example III: Control Groups		7:33
		Completely Randomized Design		9:01
			Completely Randomized Design	9:02
		Outline/Map of Completely Randomized Design		9:55
			Outline/Map of Completely Randomized Design	9:56
		Example IV: Completely Randomized Design		11:35
		Block Randomization		14:23
			Block Randomization	14:24
		Randomized Block Design		15:29
			Randomized Block Design	15:30
		Example V: Randomized Block Design		18:06
		Matched Pairs Design		21:08
			Matched Pairs Design	21:09
		Example V: Types of Experiments		22:42
		Example VI: Types of Experiments		24:17
		Example VII: Types of Experiments		26:24
		Experimental Set Up		28:28
			Treatment	28:29
			Experimental Units	29:13
			Response	29:32
		Double-blind Experiment		31:06
			Double-blind Experiment	31:07
		Example VIII: Double-blind Experiment		32:37
		Example IX: Design a Study to Test Hypothesis		37:04
		Generalizability of Results		40:39
			Statistically Significant Data	40:40
Section 4: Probability & Expected Value
	Probability Overview			1:22:17
		Intro		0:00
		Objectives		0:21
		Interpreting Probability		0:46
			Probability of a Random Outcome or the Long Term Relative Frequency	0:47
		Law of Large Numbers		1:42
			Expected Value	1:43
		Example I: Probability in Poker		2:21
		Probability Model		4:31
			Sample Space (S)	4:32
			Event	5:15
			Probabilities	6:03
		Example II: Basketball Free Throws		6:37
			Part 1: Sample Space	6:46
			Part 2: Event	8:08
			Part 3: Probability	8:48
		Disjoin Events (aka Mutually Exclusive)		11:00
			Disjoin Events (aka Mutually Exclusive)	11:01
		Example III: Advertising Contracts		12:23
			Part A: Venn Diagram	12:24
		Probability of Disjoin Events		14:03
			Probability of Disjoin Events	14:04
		Example IV: Probability of Disjoin Events		15:58
		Independence vs. Dependence		18:11
			Independence vs. Dependence	18:12
		Example V: Independence vs. Dependence		20:26
		Example VI: Independence vs. Dependence		22:23
		Probability Rules		23:13
			Probability Rules	23:14
		Probability Notation		23:31
			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 Notation		25:17
		Probability Rule Notation		26:49
			A or B	26:50
			A and B	27:40
		Example VIII: Determine if These Two Events are Independent		29:05
		Example IX: Conditional Probability of Wining		31:39
		Example X: Conditional Probability of Students		36:46
			Part A: Probability	36:47
			Part B: Conditional Probability	38:18
			Part C: Conditional Probability	39:59
		Example XI: Conditional Probability of Children		42:53
			Part A: All Boys	42:54
			Part B: All Girls	44:44
			Part C: Exactly Two Boys or Exactly Two Girls	45:50
			Part D: At Least One Child of Each Sex	50:18
		Overview		52:52
			Complement	52:53
			Mutually Exclusive	53:30
			Intersection	53:49
			Union	54:44
			Independent	55:34
		Bayes Rule		56:02
			Bayes Rule	56:03
		Example XI: Probability & Bayes Rule		59:43
		Example XII: Probability & Bayes Rule		67:49
		Simulations		65:46
			Simulations	65:47
		Example XIII: Simulations		67:10
	Intro to Probability for Discrete Random Variables			31:37
		Intro		0:00
		Objectives		0:09
		Discrete vs. Continuous Random Variables		0:29
			Discrete Random Variables	0:30
			Continuous Random Variables	1:12
		Probability Distribution		3:36
			Probability Distribution for a Discrete Random Variables	3:37
			Probability Rules	4:20
		Example I: Find the Probability		4:51
		Example II: Construct a Probability Distribution		6:15
		Mean		9:35
			Expected Value	9:36
			Example: Expected Number of Customers	10:08
		Variance		13:19
			Variance	13:20
			Example: Variance	14:34
		Example III: Probability Analysis		18:01
		Example IV: Expected Profit		25:25
	Discrete Random Variables			39:06
		Intro		0:00
		Objectives		0:08
		Binomial Distribution		0:14
			BINP	0:15
			B	0:34
			I	0:49
			N	1:00
			P	1:20
		Example I: Binomial Distribution		1:43
			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 Probability		5:11
			Binompdf (n, p, x)	5:12
		Example II: Determine the Probability		10:37
			Part A: Determine the Probability that Exactly One of the Toasters is Defective	10:38
			Part B: Determine the Probability that At Most Two of the Toasters are Defective	16:40
			Part C: Determine the Probability that More Than Three of the Toasters are Defective	21:42
		Geometric Distribution		24:11
			Geometric Distribution	24:12
		Example III: Geometric Distribution & Probability		25:14
			Part A: Geometric Distribution	25:15
		Geometric Probability		26:55
			Geometpdf (p, x)	26:56
		Example III: Geometric Distribution & Probability		27:50
			Part B: Geometric Probability of Exactly Four Patients	27:51
			Part C: Geometric Probability of At Most Five Patients	31:19
		Mean and SDs		33:47
			Binomial	33:48
			Geometric	34:28
		Example IV: Defective Units		34:53
		Example V: Number of Patients		35:58
	Combining Independent Random Variables			18:56
		Intro		0:00
		Objectives		0:09
		Mean and Standard Deviation of Two Random Variables		0:26
			Mean and Standard Deviation of Two Random Variables	0:27
		Example I: Average and Standard Deviation		1:58
		Example II: Average and Standard Deviation		4:37
		Transforming Random Variables: “Linear Transformations”		6:10
			Transforming Random Variables: “Linear Transformations”	6:11
		Example III: Mean and Standard Deviation		7:02
		Example IV: Mean and Standard Deviation		10:23
		Example V: Mean and Standard Deviation		14:14
			Part 1: Mean & SD	14:15
			Part 2: Mean & SD	16:30
	Normal Random Variables			59:34
		Intro		0:00
		Objectives		0:08
		The Empirical Rule		0:28
			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 Rule		3:24
		Z-Score		8:17
			Z-Score	8:18
		Example II: Z-Score		10:08
		Using the Normal Table		13:03
			Using the Normal Table	13:04
			Using the Normal Table, Cont.	15:05
		Example III: Using the Normal Table and Z-score to Calculate Probability		16:01
			Step 1: Sketch	16:02
			Step 2: Calculate Z-score	18:16
			Step 3: Solve for Probability Using the Normal Table	19:14
		Example IV: Using the Normal Table and Z-score to Calculate Probability		20:29
			Step 1: Sketch	20:30
			Step 2: Calculate Z-score	21:52
			Step 3: Solve for Probability Using the Normal Table	22:36
		Example V: Using the Normal Table and Z-score to Calculate Probability		27:20
			Step 1: Sketch	27:42
			Step 2: Calculate Z-score	28:14
			Step 3: Solve for Probability Using the Normal Table	29:45
		Example VI: Using the Normal Table and Z-score to Calculate Probability		34:00
			Step 1: Sketch	34:01
			Step 2: Calculate Z-score	35:48
			Step 3: Solve for Probability Using the Normal Table	36:56
		Example VII: Using the Normal Table and Z-score to Calculate Probability		41:21
			Step 1: Sketch	41:22
			Step 2: Calculate Z-score	44:15
			Step 3: Solve for Probability Using the Normal Table	47:26
		Example VIII: Calculate the Standard Deviation of the Random Normal Variable		49:54
			Step 1: Sketch	49:55
			Step 2: Calculate Z-score	51:16
			Step 3: Solve for Standard Deviation	53:16
		Example VIII: Calculate the Mean of the Distribution		55:11
			Step 1: Sketch	55:12
			Step 2: Calculate Z-score	56:36
			Step 3: Solve for Mean	57:42
Section 6: Distribution of Data
	Sampling Distributions			38:27
		Intro		0:00
		Objectives		0:07
		Parameter vs. Statistics		0:25
			Parameter vs. Statistics	0:26
		Sampling Distribution		2:03
			Sampling Distribution	2:04
		Central Limit Theorem		3:15
			Central Limit Theorem	3:16
			Central Limit Theorem, Cont.	7:23
		Example I: Sampling Distribution Graph		9:20
		Conditions (RIN)		11:12
			Random	11:13
			Independent	12:04
			Normal	13:40
		Sampling Distribution of a Sample Mean		15:19
			Sampling Distribution of a Sample Mean	15:20
		Example II: Calculate the Mean and SD of a Sampling Distribution		17:17
		Sampling Distribution of a Sample Proportion		21:07
			Sampling Distribution of a Sample Proportion	21:08
		Example III: Mean, SD, Sample Size, and Probability of a Sampling Distribution		22:29
			Part A: Calculate the Mean and SD of a Sampling Distribution	22:30
			Part B: Sample Size	26:18
			Part C: Probability	29:30
		Example IV: Probability of a Sampling Distribution		33:40
			Part A: Probability of a Random Selection	33:41
			Part B: Probability of the Mean	35:46
Section 7: Statistical Inference
	Confidence Intervals			56:37
		Intro		0:00
		Lesson Overview		0:07
		Why Calculate a Confidence Interval?		0:28
			Using a Statistic to Estimate a Parameter	0:29
		What is a Confidence Interval?		1:24
			Confidence Interval	1:25
		General math Behind a Confidence Interval		2:51
			Point Estimate	2:52
			Critical Value	4:34
		Z-Table		6:06
			Z-Table	6:07
		T-Table		7:07
			T-Table	7:08
		General math Behind a Confidence Interval		7:50
			Point Estimate	7:51
			Critical Value: Mean & Proportion	8:00
			Standard Error: Mean & Proportion	8:15
			Calculating Using Your Calculator	10:46
		Steps to Calculating a Confidence Interval		12:09
			Step 1: Read	12:10
			Step 2: Check Your Conditions	12:58
			Step 3: Calculate	15:33
			Step 4: Interpret	16:12
		Example I: Confidence Interval		16:29
		Example II: Confidence Interval		29:57
		Example III: Confidence Interval		42:31
	Hypothesis Testing			1:12:16
		Intro		0:00
		Lesson Overview		0:07
		Why do a Hypothesis Test?		0:29
			Using a Statistic to Test a Claim about a Parameter	0:30
		Steps for Calculating a Hypothesis Test		1:13
			1. Write the Hypothesis	1:14
			2. Check Conditions	1:30
			3. Calculate the Test Statistic	1:34
			4. Look Up the P-value & Interpret	1:49
			5. Interpret	1:50
		Example I: Hypothesis Testing Step by Step		2:57
			1. Write the Hypothesis	5:04
			2. Check Conditions	8:43
			3. Calculate the Test Statistic	21:54
			4. Look Up the P-value	20:07
			5. Interpret	23:45
		Example II: Hypothesis Testing Step by Step		28:49
			1. Write the Hypothesis	28:50
			2. Check Conditions	32:00
			3. Calculate the Test Statistic	34:20
			4. Look Up the P-value	38:26
			5. Interpret	40:49
		Example III: Hypothesis Test for a Mean		44:53
		Example IV: Hypothesis Test for a Proportion		57:26
	The T Distribution			41:40
		Intro		0:00
		Lesson Overview		0:07
		When Do We Use the T Distribution		0:26
			When Do We Use the T Distribution	0:27
		What is the T Distribution?		1:46
			What is the T Distribution?	1:47
		Confidence Interval Example		2:49
			Construct and Interpret a 90% Confidence Interval to Estimate the Mean	2:50
		Hypothesis Test Example		16:59
			1. Write the Hypothesis	17:00
			2. Check Conditions	20:01
			3. Calculate the Test Statistic	21:24
			4. Look Up the P-value	24:39
			5. Interpret	27:23
		Matched Pairs T-test		29:34
			Matched Pairs T-test	29:35
			1. Write the Hypothesis	33:05
			2. Check Conditions	34:58
			3. Calculate the Test Statistic	35:52
			4. Look Up the P-value	38:12
			5. Interpret	39:28
	Two Samples			1:27:23
		Intro		0:00
		Lesson Overview		0:09
		What Will a 2 Sample Problem Look Like?		0:40
			Example 1	0:41
			Example 2	2:01
		Writing Your Hypothesis		3:36
			Writing Your Hypothesis	3:37
		Hypothesis Test Example I		7:02
			1. Write the Hypothesis	7:03
			2. Check Conditions	10:04
			3. Calculate the Test Statistic	13:21
			4. Look Up the P-value	20:54
			5. Interpret	22:48
		Hypothesis Test Example II		24:50
			1. Write the Hypothesis	24:51
			2. Check Conditions	28:34
			3. Calculate the Test Statistic	29:46
			4. Look Up the P-value	36:27
			5. Interpret	39:01
		Example I: Two Samples Hypothesis Testing		42:11
		Example II: Two Samples Hypothesis Testing		53:30
		“Pick Your Test” Map		70:47
			“Pick Your Test” Map	70:48
		Example III: Reliability Testing		78:31
	Hypothesis Testing of Least-Squares Regression Line			53:49
		Intro		0:00
		Lesson Overview		0:10
		Review of Least-squares Regression and Interpretation		0:29
			Correlation Coefficient ( r )	0:30
			Equation of the Least-squares Regression Line	1:02
		Example		2:45
			Part A: Least-squares Regression Line	2:46
			Part B: Slope of the Least-squares Regression Line	6:03
		Test for the Regression Line		7:50
			Is There a Correlation?	7:51
			Is the y-intercept = 0?	9:56
		Conditions for Hypothesis Testing		10:49
			Linearity	11:27
			Constant Variability	12:35
			Normality	13:40
			Independence	15:16
		Hypothesis Testing		16:10
			Standard Deviation of the Residuals	16:11
			Standard Error of Slope	17:30
			Test Statistic	18:45
			Confidence Interval	19:36
		Example: Hypothesis Testing		20:45
			Part A: Test the Hypothesis	20:46
			Part B: 95% Confidence Interval of the Slope	32:51
		Interpreting Computer Output		35:40
			Interpreting Computer Output	35:41
		Example I: Interpreting Computer Output		38:46
			Part A: Least-squares Regression Equation	38:47
			Part B: Standard Error	40:01
			Part C: Slope of the Least-squares Regression Line	41:21
			Part D: Null and Alternative Hypotheses	42:08
			Part E: Value of Test Statistic	43:09
			Part G: P-Value	44:03
			Part H: Is Income Useful for Predicting the Cost of a Person’s Car?	45:46
			Part I: Estimated Cost	46:57
		Example II: Interpreting Computer Output		47:48
	Hypothesis Tests for Categorical Data (Chi-Squared Tests)			1:12:55
		Intro		0:00
		Lesson Overview		0:11
		How Do We Know to Use a Chi-Squared Test?		0:27
			Categorical Data	0:28
		Chi-Squared Goodness of Fit Test		1:50
			One Categorical Variable with Counts in Each Category	1:51
			What We Have Seen	2:17
			New Question Type	2:56
		Example I: Chi-Squared Goodness of Fit Test		4:02
			Chi-Squared Goodness of Fit Steps Overview	4:03
			Step 1: Hypothesis	5:54
			Step 2: Expected	7:42
			Step 3: Conditions	10:34
			Step 4: Calculate	11:44
			Step 5: P-Value & Chi-Square Distribution Table	17:03
		Example II: Chi-Squared Goodness of Fit Test		22:04
			Step 1: Hypothesis	22:05
			Step 2: Expected	24:55
			Step 3: Calculate	29:05
			Step 4: P-Value & Chi-Square Distribution Table	33:18
		Chi-Squared Test of: Homogeneity or Independence/Association		34:31
			Homogeneity	34:32
			Independence/Association	35:42
		Example III: Chi-Squared Test of: Homogeneity or Independence/Association		37:55
			Step 1: Hypothesis	37:56
			Step 2: Expected	40:28
			Step 3: Conditions	46:48
			Step 4: Calculate	47:49
			Step 5: P-Value & Chi-Square Distribution Table	49:30
		As a Test of Association		52:53
			As a Test of Association	52:54
		Example IV: Chi-Squared Test of: Homogeneity or Independence/Association		55:05
			Step 1: Hypothesis, Expected, and Conditions	55:06
			Step 2: Calculate	59:45
			Step3: P-Value & Chi-Square Distribution Table	61:51
		Example V: Chi-Squared Test of: Homogeneity or Independence/Association		62:48
			Step 1: Hypothesis	62:49
			Step 2: Expected and Conditions	65:12
			Step 3: Calculate	66:36
			Step 4: P-Value & Chi-Square Distribution Table	70:50
Section 8: AP Practice Test
	Practice Test 2013 AP Statistics			1:02:57
		Intro		0:00
		Question 1		0:23
			Question 1: Part A	0:24
			Question 1: Part B	2:10
		Question 2		6:16
			Question 2: Part A	6:17
			Question 2: Part B	10:22
			Question 2: Part C	12:09
		Question 3		14:30
			Question 3: Part A	14:31
			Question 3: Part B	18:19
		Question 4		24:49
			Question 4: Part A	24:50
		Question 5		37:27
			Question 5: Part A	37:28
			Question 5: Part B	42:32
		Question 6		51:15
			Question 6: Part A	51:16
			Question 6: Part B	55:17
	Practice Test 2014 AP Statistics			1:00:07
		Intro		0:00
		Question 1		0:32
		Question 2		9:46
			Question 2: Part A	9:47
			Question 2: Part B	12:28
			Question 2: Part C	13:22
		Question 3		15:38
			Question 3: Part A	15:39
			Question 3: Part B	18:40
		Question 4		27:33
			Question 4: Part A	27:34
			Question 4: Part B	30:05
		Question 5		34:15
			Question 5: Part 1	34:16
			Question 5: Part 2	37:29
			Question 5: Part 3	39:50
			Question 5: Part 4	40:59
			Question 5: Part 5	44:09
		Question 6		45:30

Duration: 15 hours, 04 minutes

Number of Lessons: 18

This online AP Statistics course is not only perfect for high school students enrolled in the class and taking the test, but is also ideal for college students taking general statistics as well. Professor Nelson covers all the topics of the AP Statistics exam with tons of examples, as well as how to use the popular TI graphing calculators in statistical analysis.

Additional Features:

Free Sample Lessons
Downloadable Lecture Slides
Instructor Comments

Topics Include:

Correlation and Regression
Planning and Conducting Experiments
Probability for Discrete Random Variables
Sampling Distributions
Confidence Intervals
Hypothesis Testing
Least Square Regression Lines
Chi-Squared Tests

Professor Sandahl Nelson has been teaching statistics for over 8 years, with experience as a private tutor, college instructor, and high school teacher teaching students how to ace AP Statistics. She received her B.S. from UC San Diego, her M.S. in Biology from UC San Diego, her M.S. in Biostatistics from UCLA, and is currently finishing up her Ph.D. in Epidemiology and Biostatistics from UC San Diego.