Chapter 1 - Histograms & Charting………………………………………………….....

• Creating a Chart………………………………………………………………………………..

• Creating Descriptive Statistics…………………………………………….....………………….

• Creating a Histogram………………………………………………………......……………….
   
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Chapter 2 - Combinations & Permutations…………………………………………..

• Basic Explanation of Combinations and Permutations……………………………………………
• Difference Between Combinations and Permutations……………………………………………
• Combination Formulas……………………………………………………………….…………
• Excel Functions Used When Calculating Combinations……………………………….…………
• COMBIN (n,x)………………………………………………………………………...………
• FACT (n)………………………………………………………………………………………
• Permutation Formulas……………………………………………………………..……………
• Excel Functions Used When Calculating Permutations……………………………..……………
• PERMUT (n, x)…………………………………………………………………..……………
• FACT (n)………………………………………………………………………………………

• Combination Problems…………………………………………………………………………
• Problem 1: Combinations of Investment Proposals………………………………………………
• Problem 2: Combination of Newly Opened Offices……………………………………………
• Problem 3: Combinations of Multiple Newly Opened Offices……………………………………
• Problem 4: Combinations of Committees………………………………………………………
• Problem 5: Combinations of Sub-Groups………………………………………………………

• Permutation Problems…………………………………………………………………………
• Problem 6: Permutations of Delivery Routes……………………………………………………
• Problem 7: Permutations of Seating Arrangements………………………………………………
• Problem 8: Permutations of Executive Groups…………………………………………………
• Problem 9: Permutations of Book Arrangements………………………………………………
• Problem 10: Permutations of Letter Groups……………………………………………………
  
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Chapter 3 - Correlation & Covariance………….……………………………………..

• Basic Explanation of Correlation and Covariance……………….………………………………
• Correlation Analysis……………………………………………....……………………………
• Positive Correlation vs. Negative Correlation……………………………………………………
• Calculation of Correlation Coefficient………………………………...…………………………
• Excel Functions Used When Calculating Correlation Coefficient…………………………………
• CORREL (Highlighted Blocks of Cells of 2 Variables)……………….…………………………
• Problem 1: Calculating Correlation Between 2 Variables …………….…………………………
• Tools / Data Analysis / Correlation……………………………………...………………………
• Problem 2: Calculating Correlation Between Multiple Variables…………....……………………
• Covariance Analysis……………………………………………………………………………
• Calculation of Covariance Page……………………………………………...…………………
• Excel Functions Used When Calculating Covariance……………………………………………
• COVAR (Highlighted Blocks of Cells of 2 Variables).…………………………..………………
• Problem 3: Calculating Covariance Between 2 Variables……………………......………………
• Tools / Data Analysis / Covariance………………………………………………..……………
• Problem 4: Calculating Covariance Between Multiple Variables………………………………   
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Chapter 4 - Normal Distribution………….……………………….……………………

• Basic Description of Normal Distribution…………………………………..……………………
• Mapping the Normal Curve……………………………………………….……………………
• The Standardized Normal Curve………………………………………….……………………
• The "68 - 95 - 99.7%" Rule……………………………………………….……………………
• "Six Sigma Quality" in the Corporate World……………………………….……………………
• The 4 Most Important Excel Normal Curve Functions………………………………..…………
• NORMDIST (x, Mean, Standard Dev, TRUE)…………………………………………………
• NORMSDIST (x)………………………………………………………...……………………
• NORMINV (% of area to left of x, Mean, Standard Deviation)…………………………………
• NORMSINV (% of area to the left of x)…………………………………..……………………
• Problem 1: Using the Normal Distribution to Determine Probability of Daily Sales
    Below a Certain Point……………………………………………………….……...…………
• Problem 2 : Using the Normal Distribution to Determine Probability that Fuel
    Consumption is in a Certain Range…..…………………………………………………………
• Problem 3: Using the Normal Distribution to Determine Upper Limit of
    Delivery Time………….....……………………………………………………………………
• Problem 4: Using Normal Distribution to Determine Lower Limit of Tire Life……………….……
• Problem 5: Using Normal Distribution to Determine Boundaries of a Range
    of Tire Life…….………………………………………………………………………………
• Problem 6: Using Normal Distribution to Determine Probability of a Pumpkin's
    Weight Being in 1 of 2 Ranges……....…………………………………………………………

   
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Chapter 5 - Normal Distribution….....................………………………………………..

• Basic Description of t Distribution………………………………………………………………
• Degrees of Freedom……………………………………………………………………………
• One Very Important Caution About Using the t Distribution…………………………………..…
• The Normal Distribution and Large Samples………………………………………….…………
• Estimating Confidence Intervals with the t Distribution………………………………...…………
• Levels of Confidence and Significance……………………………………………..……………
• Population Mean vs. Sample Mean…………………………………………………..…………
• Standard Deviation and Standard Error…………………………………………………………
• Region of Certainty vs. Region of Uncertainty………………………………...…………………
• t Value…………………………………………………………………………………………
• Excel Functions Used When Calculating Confidence Interval……………………………………
• COUNT (Highlighted Block of Cells)…………………………………..………………………
• STDEV (Highlighted Block of Cells)………………………………....…………………………
• AVERAGE (Highlighted Block of Cells)…………………………..……………………………
• TINV (a)…………………………………………………….………………………………
• Formula for Calculating Confidence Interval Boundaries………...………………………………
• Problem: Calculate a Confidence Interval Based on Small Sample
    Data Using the t Distribution………………………...…………………………………………
• t Test and Hypothesis Testing……………………...……………………………………………

   
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Chapter 6 - Binomial Distribution....................................................................................

• Basic Explanation of Binomial Distribution………………………………………………………
• Bernoulli Trial…………………………………………………………..………………………
• Bernoulli Process……………………………………………………….………………………
• Bernoulli Distribution……………………………………………………………………………
• Binomial Distribution Parameters…………………………………………..……………………
• Random Variable………………………………………………………….……………………
• Count of Successes per Trial……………………………………………………………………
• Population Proportion………………………………………………………..…………………
• Sample Proportion……………………………………………………………...………………
• Sample Size…………………………………………………………………….………………
• Expected Sample Occurrence Parameters………………………………………………………
• Expected Sample Occurrence Mean……………………………………………....……………
• Expected Sample Occurrence Variance……………………………………………...…………
• Expected Sample Occurrence Standard Deviation………………………………………………
• Expected Sample Proportion Parameters…………………………………………….....………
• Expected Sample Proportion……………………………………………………………...……
• Expected Sample Proportion Variance……………………………………………….…………
• Expected Sample Proportion Standard Deviation……………………………………….………
• Probability Density Function vs. Cumulative Distribution Function…………………….…………
• Binomial Probability Density Function…………………………………………………...………
• BINOMDIST (k, n, p, FALSE)………………………………………………………......……
• Binomial Cumulative Distribution Function………………………………………………………
• BINOMDIST (k, n, p, TRUE)…………………………………………………………………
• Problem 1: Probability of Getting a Certain Number of Successes for Binomial Variable Trials…...
• Problem 2: Probability of Getting a Certain Range of Successes for Binomial Variable Trials……..
• Problem 3: Probability of Getting a Certain Range of Successes for Binomial Variable Trials……..
• Estimating the Binomial Distribution with the Normal and Poisson Distributions…………………..
• Basic Explanation of Combinations and Permutations....................................................................

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Chapter 7 - Confidence Intervals......................................................................................

• Basic Explanation of Confidence Intervals………………………………………………………
• Mean Sampling vs. Proportion Sampling…………………………..……………………………
• Confidence Intervals of a Population Mean…………………………...…………………………
• Calculate Confidence Intervals Using Large Samples……………………………………………
• The Central Limit Theorem……………………………………………..………………………
• Levels of Confidence and Significance…………………………………..………………………
• Population Mean vs. Sample Mean………………………………………..……………………
• Standard Deviation and Standard Error…………………………………………………………
• Region of Certainty vs. Region of Uncertainty………………………………...…………………
• Z Score………………………………………...………………………………………………
• Excel Functions Used When Calculating Confidence Interval of Mean……………...……………
• COUNT (Highlighted Block of Cells)………………………………………………...…………
• STDEV (Highlighted Block of Cells)……………………………………………………………
• AVERAGE (Highlighted Block of Cells)………………………………………………..………
• NORMSINV (1 - a/2)…………………………………………………………………………
• CONFIDENCE (a, s, n)……………………………………………………………….………
• Formulas for Calculating Confidence Interval Boundaries from Sample Data…………….………
• Problem 1: Calculate a Confidence Interval from a Random Sample of Test Scores………..……
• Problem 2: Calculate a Confidence Interval of Daily Sales Based Upon Sample
    Mean and Standard Deviation…………………………………………………........…………
• Problem 3: Calculate an Exact Range of 95% of Sales Based Upon the
    Population Mean and Standard Deviation……………………………………...………………
• Determine Minimum Sample Size to Limit Confidence Interval of Mean to a
    Certain Width…………………………………………………………………………………
• Problem 4: Determine the Minimum Number of Sales Territories to Sample
    In Order To Limit the 95% Confidence Interval to a Certain Width………………….....………
• Confidence Interval of a Population Proportion…………………………………………………
• Mean Sampling vs. Proportion Sampling………………………………………..………………
• Levels of Confidence and Significance…………………………………………..………………
• Standard Deviation and Standard Error…………………………………………………………
• Region of Certainty vs. Region of Uncertainty……………………………………...……………
• Z Score…………………………………………………………………………...……………
• Excel Functions Used When Calculating Confidence Interval of Proportion…………...…………
• COUNT (Highlighted Block of Cells)…………………………………………………..….……
• NORMSINV (1 - a)……………………………………………………………………...……
• Formula for Calculating Confidence Interval Boundaries from Sample Data…………………...…
• Problem 5: Determine Confidence Interval of Shoppers Who Prefer to Pay By
    Credit Card Based Upon Sample Data……………...…………………………………………
• Determine Minimum Sample Size to Limit Confidence Interval of Proportion to a
    Certain Width…………………………………………………………………………………
• Problem 6: Determine the Minimum Sample Size of Voters to be 95% Certain
    that the Population Proportion is only 1% Different than Sample Proportion………….…………

   
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Chapter 8 - Hypothesis Tests - Means.............................................................................

• Basic Explanation of Hypothesis Testing of Means………………………………………………
• The Four-Step Method for Solving All Hypothesis Testing Problems……………………………
• The Four Ways of Classifying All Hypothesis Test Problems……………………………………
• Mean Testing vs. Proportion Testing……………………………………………………………
• One-Tailed vs. Two-Tailed Testing…………………………………………………..…………
• One Sample vs. Two Samples……………………………………………………….…………
• Unpaired Data Testing vs. Paired Data Testing……………………………………….…………
• Detailed Description of the Four-Step Method for Solving Mean Testing Problems…...…………
• Initial Steps…………………………………………………………………………..…………
• Problem Classification…………………………………………………………………..………
• Mean Testing vs. Proportion Testing……………………………………………………........…
• One-Tailed vs. Two-Tailed Testing…………………………………………..…………………
• One Sample vs. Two Sample Testing…………………………………………...………………
• Unpaired Data Testing vs. Paired Data Testing…………………………………….……………
• Information Layout……………………………………………..………………………………
• Level of Significance……………………………………………………………………………
• Comparison Sample Data………………………………………………………………………
• The Four Steps to Solving All Hypothesis Testing Problems……………….……………………
• Step 1 - Create Null and Alternate Hypotheses……………………………....…………………
• Step 2 - Map the Normal Curve…………………………………..……………………………
• Step 3 - Map the Region of Certainty……………………………………..……………………
• Mapping the Region of Certainty for a Two-Tailed Test……………………………………....…
• Mapping the Region of Certainty for a One-Tailed Test…………………………………………
• Step 4 Perform Critical Value and p-Value Tests……………….…………………….…………
• Critical Value Test………………………………………………………...……………………
• p Value Test…………………………………………………………....………………………
• Type 1 and Type 2 Errors………………………………………………………………………
• Problem 1: Two-Tailed, One Sample, Unpaired Hypothesis Test of Mean
    Testing a Manufacturer's Claim of Average Product Thickness……………………....…………
• Problem 2: One-Tailed, One Sample, Unpaired Hypothesis Test of Mean
    Testing Whether a Delivery Time Has Gotten Worse………..…………………………………
• Problem 3: Two-Tailed, Two Sample, Unpaired Hypothesis Test of Mean
    Testing Whether Wages Are the Same in Two Areas……..……………………………………
• Paired Data………………………………………………………………….…………………
• Problem 4: One-Tailed, One Sample, Paired Hypothesis Test of Mean
    Testing Whether an Advertising Campaign Improved Sales…………………………….………

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Chapter 9 - Hypothesis Tests – Proportions………………....………………………..

• Basic Explanation of Hypothesis Testing of Proportions…………………………………………
• The Four-Step Method for Solving All Hypothesis Testing Problems……………………………
• The Four Ways of Classifying All Hypothesis Test Problems……………………………………
• Mean Testing vs. Proportion Testing……………………………………………………………
• One-Tailed vs. Two-Tailed Testing…………………………………..…………………………
• One Sample vs. Two Samples……………………………………….…………………………
• Unpaired Data Testing vs. Paired Data Testing…………………………………………….……
• Detailed Description of the Four-Step Method for Solving Proportion Testing Problems…………
• Initial Steps…………………………………………..…………………………………………
• Problem Classification………………………………….…………….…………………………
• Mean Testing vs. Proportion Testing……………………………………………………………
• One-Tailed vs. Two-Tailed Testing………………………………….……….…………………
• One Sample vs. Two Sample Testing………………………………………………...…………
• Unpaired Data Testing vs. Paired Data Testing………………….....……………………………
• Information Layout…………………………………………..…………………………………
• Level of Significance……………………………....……………………………………………
• Comparison Sample Data……………………………………....………………………………
• The Four Steps to Solving All Hypothesis Testing Problems……..………………………………
• Step 1 - Create Null and Alternate Hypotheses…………………………………………………
• Step 2 - Map the Normal Curve……………………………………..…………………………
• Step 3 - Map the Region of Certainty………………….…………………..……..……..………
• Mapping the Region of Certainty for a Two-Tailed Test……………………....…………………
• Mapping the Region of Certainty for a One-Tailed Test…………………………………………
• Step 4 Perform Critical Value and p-Value Tests………………………..………………………
• Critical Value Test……………………………………….………..……………………………
• p Value Test……………………………………………………………………………………
• Type 1 and Type 2 Errors………………………………………………………………………
• Problem 1: Two-Tailed, One Sample, Unpaired Hypothesis Test of Proportion
    Testing Employee Preferences in Two Companies……………...………………………………
• Problem 2: One-Tailed, Two Sample, Unpaired Hypothesis Test of Proportion
    Testing Effectiveness of Two Drugs……………………………………………………………

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Chapter 10 - Excel Hypothesis Tools…………………….………………..……………

• t-Test: Paired Two Sample for Means…………………………………………………………

• t-Test:Two-Sample Assuming Unequal Variances………………………………………………

• t-Test: Two-Sample Assuming Equal Variances…………………………………………………

• z-Test: Two Sample for Means…………………………………………………………………

• ZTEST…………………………………………………………………………………………

• TTEST…………………………………………………………………………………………

   
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Chapter 11 - Prediction Using Regression…………………...……………..………….

• Basic Explanation of Regression……………..…………….……………………………………
• The Regression Equation………………………………….……………………………………
• Regression is for Predicting, Not Forecasting……………………………………………………
• Performing Multiple Regression in Excel…………………...……………………………………
• 1st Regression Step - Graph the Data……………………………..……………………………
• 2nd Regression Step - Run Correlation Analysis…………………...……………………………
• 3rd Regression Step - Run Regression Analysis…………………………………………………
• 4th Regression Step - Analyze the Output………………………………………………………
• The Regression Equation………………………………..………………………………………
• Using the Regression Equation to Predict an Output…….....……………………...………..……
• The Confidence Interval of the Output Variable…………………………………………………
• R Square……………………………………………………………………….………………
• Adjusted R Square……………………………………………………….….…………………
• F Statistic………………………………………………………………………………………
• ANOVA Calculation of the Regression Output…………………………………………………
• P Values of the Regression Coefficients and Intercept…..……………….………………………
• Regression Using Dummy Variables……………………………………….……………………
• Creating Dummy Variables for Attributes of Multiple Choices….…………..……………………
• Removing a Dummy Variable to Prevent Co-Linearity……………..…………..……..…………
• Conjoint Analysis Done With Regression Using Dummy Variables………………………………
• 1st Conjoint Step - List Product Attributes………………….……………..……………………
• 2nd Conjoint Step - List All Attribute Combinations……………………….……………………
• 3rd Conjoint Step - Conduct Consumer Survey…………………………...……………………
• 4th Conjoint Step - Create Dummy Variables for Attributes…………………..…………………
• 5th Conjoint Step - Remove 1 Dummy Variable from Each Set of Attributes……………………
• 6th Conjoint Step - Run Regression Analysis……………………………………………………
• 7th Conjoint Step - Analyze the Output…………………………………………………………
• Showing the Removing Dummy Variables Did Not Affect Output…....……....…….……..….….   
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Chapter 12 - Independence Tests & ANOVA…………………………………………

• Basic Explanation of ANOVA…………………………………………………………………
• ANOVA Tests the Null Hypothesis - That Nothing Is Different Between Groups……….………
• Overview of ANOVA in Excel…………………………………………………………………
• Single Factor ANOVA…………………………………………………………………………
• Two-Factor ANOVA Without Replication…………………………………………..…………
• Two-Factor ANOVA With Replication…………………………….………………..…………
• ANOVA:Single Factor Analysis……………………………………..………….………………
• Problem: 3 Sales Closing Methods and Single Factor ANOVA…………………………………
• Problem Solving Steps………………………………………………………….………………
• Analyze the Output……………………………………………………………..………………
• ANOVA: Two-Factor Without Replication………………………………..……………………
• Problem: 3 Sales Closing Methods, 5 Salespeople, and Two-Factor ANOVA
    Without Replication…………..………………………………………..………………………
• Problem Solving Steps…………………………………………………………….……………
• Analyze the Output…………………………………………………………………..…………
• ANOVA:Two Factor With Replication…………………………………………………………
• Problem: 3 Ad Headlines, 3 Ad Texts, their Interaction, and Two-Factor ANOVA
    With Replication………………………………………………………………………………
• Problem Solving Steps…………………………………………………….……………………
• Analyze the Output………………………………………………….…….……………………
• ANOVA: Single Factor Analysis Calculated by Hand……………………..………….…………
• Problem: 3 Closing Methods and Single Factor ANOVA Calculated By Hand…………..………


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Chapter 13 - Chi-Square Independence Test…………………………………………

• Basic Explanation of the Chi-Square Independence Test……………………………..…………
• Level of Certainty………………………………………………………………………………
• Level of Significance……………………………………………………………………………
• Contingency Table……………………………………………………………………...………
• Degrees of Freedom……………………………………………………………………………
• Chi-Square Distribution…………………………………………………...……………………
• Critical Chi-Square Statistic……………………………………………….……………………
• Independence Test Rule……………………………………………………...…………………
• Excel Functions Used When Performing the Chi-Square Independence Test….....………………
• CHIINC (Level of Significance, Degrees of Freedom)…………………………..………………
• CHIDIST (Critical Chi-Square Statistic, Degrees of Freedom)………………….………………
• Problem: Determine if There is a Relationship Between the Time Spent in a
Store and the Amount of Items Purchased….………………………………………...………..…
   
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Chapter 14 - Variance of Population Test…………………………………..…………

• Basic Explanation of the Chi-Square Variance Change Test………………………..……………
• The 5-Step Chi-Square Variance Change Test……………………………………….…………
• 1st Variance Test Step - Determine the Level of Certainty and a……………………...…………
• 2nd Variance Test Step - Measure Sample Standard Deviation…………………………………
• 3rd Variance Test Step - Calculate the Chi-Square Statistic………………………….……….…
• 4th Variance Test Step - Calculate the Curve Area to the Outside of the Chi-Square Statistic....…
• 5th Variance Test Step - Analyze Results Using the Chi-Square Statistic Rule…………...………
• Problem: Using Chi-Square Test to Determine Whether Population Variance has Increased…..…
• Apply the 5-Step Chi-Square Variance Change Test……………………………………………
• Problem: Using Chi-Square Test to Determine Whether Population Variance has Decreased….…
• Apply the 5-Step Chi-Square Variance Change Test……………………………………………

   
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Chapter 15 - Other Useful Distributions………………………………….....…………
 
• Multinomial Distribution…………………………………...……………………………………
• Hypergeometric Distribution……………………………………………………………………
• Poisson Distribution……………………………………….……………………………………
• Uniform Distribution……………………………………….……………………………………
• Exponential Distribution……………………………………...…………………………………
• Gamma Distribution………………………………………….…………………………………
• Beta Distribution……………………………………………..…………………………………
• Weibull Distribution………………………………………….…………………………………
• F Distribution…………………………………………………...………………………………   
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Chapter 16 - How To Graph Distributions………………………………....…………
 
1) Learning how to graph a generic set of x-y coordinates……………..….………………………
.
2) Learning how to create the x coordinates and the y coordinates
specific to the type of distribution being graphed………………….………………………………

• Normal Distribution……………………………………………………….……………………
• Probability Density Function……………………………………………….……………………
• Cumulative Distribution Function……………………………………………..…………………

• Normal Distribution - Graphing Outer 2% Tails…………………………………………………
• Probability Density Function………………………………………………….…………………

• t Distribution……………………………………………………………………………………
• Probability Density Function……………………………………………………………………

• Binomial Distribution……………………………………………………………………………
• Probability Density Function…………………………………………………………….………
• Cumulative Distribution Function…………………………………………………………..……

• Chi-Square Distribution……………………………………………………………………...…
• Probability Density Function……………………………………………………………………

• Poisson Distribution…………………………………………………………………….………
• Probability Density Function…………………………………………………………….………
• Cumulative Distribution Function………………………………………………………..………

• Weibull Distribution……………………………………………………………………….……
• Probability Density Function…………………………………………………………….………
• Cumulative Distribution Function…………………………………………………………..……

• Exponential Distribution……………………………………………………………...…………
• Probability Density Function……………………………………………………………………
• Cumulative Distribution Function…………………………………………………….…………

• Hypergeometric Distribution……………………………………………………………………
• Probability Density Function……………………………………………………………………

• Beta Distribution……………………………………………………………………......………
• Cumulative Distribution Function…………………………………………………………..……

• Gamma Distribution……………………………………………………………………….……
• Probability Density Function……………………………………………………………….……
• Cumulative Distribution Function……………………………………………………………..…

   
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