;Advance Praise for Head First Statistics;Praise for other Head First books;Author of Head First Statistics;How to use this Book: Intro; Who is this book for?; We know what you''re thinking; We know what your brain is thinking; Metacognition: thinking about thinking; Here''s what WE did; Read Me; The technical review team; Acknowledgments; Safari® Books Online;Chapter 1: Visualizing Information: First Impressions; 1.1 Statistics are everywhere; 1.2 But why learn statistics?; 1.3 A tale of two charts; 1.4 Manic Mango needs some charts; 1.5 The humble pie chart; 1.6 Chart failure; 1.7 Bar charts can allow for more accuracy; 1.

8 Vertical bar charts; 1.9 Horizontal bar charts; 1.10 It''s a matter of scale; 1.11 Using frequency scales; 1.12 Dealing with multiple sets of data; 1.13 Your bar charts rock; 1.14 Categories vs. numbers; 1.

15 Dealing with grouped data; 1.16 To make a histogram, start by finding bar widths; 1.17 Manic Mango needs another chart; 1.18 Make the area of histogram bars proportional to frequency; 1.19 Step 1: Find the bar widths; 1.20 Step 2: Find the bar heights; 1.21 Step 3: Draw your chart--a histogram; 1.22 Histograms can''t do everything; 1.

23 Introducing cumulative frequency; 1.24 Drawing the cumulative frequency graph; 1.25 Choosing the right chart; 1.26 Manic Mango conquered the games market!;Chapter 2: Measuring Central Tendency: The Middle Way; 2.1 Welcome to the Health Club; 2.2 A common measure of average is the mean; 2.3 Mean math; 2.4 Dealing with unknowns; 2.

5 Back to the mean; 2.6 Handling frequencies; 2.7 Back to the Health Club; 2.8 Everybody was Kung Fu fighting; 2.9 Our data has outliers; 2.10 The butler outliers did it; 2.11 Watercooler conversation; 2.12 Finding the median; 2.

13 Business is booming; 2.14 The Little Ducklings swimming class; 2.15 Frequency Magnets; 2.16 Frequency Magnets; 2.17 What went wrong with the mean and median?; 2.18 Introducing the mode; 2.19 Congratulations!;Chapter 3: Measuring Variability and Spread: Power Ranges; 3.1 Wanted: one player; 3.

2 We need to compare player scores; 3.3 Use the range to differentiate between data sets; 3.4 The problem with outliers; 3.5 We need to get away from outliers; 3.6 Quartiles come to the rescue; 3.7 The interquartile range excludes outliers; 3.8 Quartile anatomy; 3.9 We''re not just limited to quartiles; 3.

10 So what are percentiles?; 3.11 Box and whisker plots let you visualize ranges; 3.12 Variability is more than just spread; 3.13 Calculating average distances; 3.14 We can calculate variation with the variance.; 3.15 .but standard deviation is a more intuitive measure; 3.

16 A quicker calculation for variance; 3.17 What if we need a baseline for comparison?; 3.18 Use standard scores to compare values across data sets; 3.19 Interpreting standard scores; 3.20 Statsville All Stars win the league!;Chapter 4: Calculating Probabilities: Taking Chances; 4.1 Fat Dan''s Grand Slam; 4.2 Roll up for roulette!; 4.3 Your very own roulette board; 4.

4 Place your bets now!; 4.5 What are the chances?; 4.6 Find roulette probabilities; 4.7 You can visualize probabilities with a Venn diagram; 4.8 It''s time to play!; 4.9 And the winning number is.; 4.10 Let''s bet on an even more likely event; 4.

11 You can also add probabilities; 4.12 You win!; 4.13 Time for another bet; 4.14 Exclusive events and intersecting events; 4.15 Problems at the intersection; 4.16 Some more notation; 4.17 Another unlucky spin.; 4.

18 .but it''s time for another bet; 4.19 Conditions apply; 4.20 Find conditional probabilities; 4.21 You can visualize conditional probabilities with a probability tree; 4.22 Trees also help you calculate conditional probabilities; 4.23 Bad luck!; 4.24 We can find P(Black l Even) using the probabilities we already have; 4.

25 Step 1: Finding P(Black ∩ Even); 4.26 So where does this get us?; 4.27 Step 2: Finding P(Even); 4.28 Step 3: Finding P(Black l Even); 4.29 These results can be generalized to other problems; 4.30 Use the Law of Total Probability to find P(B); 4.31 Introducing Bayes'' Theorem; 4.32 We have a winner!; 4.

33 It''s time for one last bet; 4.34 If events affect each other, they are dependent; 4.35 If events do not affect each other, they are independent; 4.36 More on calculating probability for independent events; 4.37 Winner! Winner!;Chapter 5: Using Discrete Probability Distributions: Manage Your Expectations; 5.1 Back at Fat Dan''s Casino; 5.2 We can compose a probability distribution for the slot machine; 5.3 Expectation gives you a prediction of the results.

; 5.4 . and variance tells you about the spread of the results; 5.5 Variances and probability distributions; 5.6 Let''s calculate the slot machine''s variance; 5.7 Fat Dan changed his prices; 5.8 There''s a linear relationship between E(X) and E(Y); 5.9 Slot machine transformations; 5.

10 General formulas for linear transforms; 5.11 Every pull of the lever is an independent observation; 5.12 Observation shortcuts; 5.13 New slot machine on the block; 5.14 Add E(X) and E(Y) to get E(X + Y).; 5.15 . and subtract E(X) and E(Y) to get E(X - Y); 5.

16 You can also add and subtract linear transformations; 5.17 Jackpot!;Chapter 6: Permutations and Combinations: Making Arrangements; 6.1 The Statsville Derby; 6.2 It''s a three-horse race; 6.3 How many ways can they cross the finish line?; 6.4 Calculate the number of arrangements; 6.5 Going round in circles; 6.6 It''s time for the novelty race; 6.

7 Arranging by individuals is different than arranging by type; 6.8 We need to arrange animals by type; 6.9 Generalize a formula for arranging duplicates; 6.10 It''s time for the twenty-horse race; 6.11 How many ways can we fill the top three positions?; 6.12 Examining permutations; 6.13 What if horse order doesn''t matter; 6.14 Examining combinations; 6.

15 It''s the end of the race;Chapter 7: Geometric, Binomial, and Poisson Distributions: Keeping Things Discrete; 7.1 Meet Chad, the hapless snowboarder; 7.2 We need to find Chad''s probability distribution; 7.3 There''s a pattern to this probability distribution; 7.4 The probability distribution can be represented algebraically; 7.5 The pattern of expectations for the geometric distribution; 7.6 Expectation is 1/p; 7.7 Finding the variance for our distribution; 7.

8 You''ve mastered the geometric distribution; 7.9 Should you play, or walk away?; 7.10 Generalizing the probability for three questions; 7.11 Let''s generalize the probability further; 7.12 What''s the expectation and variance?; 7.13 Binomial expectation and variance; 7.14 The Statsville Cinema has a problem; 7.15 Expectation and variance for the Poisson distribution; 7.

16 So what''s the probability distribution?; 7.17 Combine Poisson variables; 7.18 The Poisson in disguise; 7.19 Anyone for popcorn?;Chapter 8: Using the Normal Distribution: Being Normal; 8.1 Discrete data takes exact values.; 8.2 . but not all numeric data is discrete; 8.

3 What''s the delay?; 8.4 We need a probability distribution for continuous data; 8.5 Probability density functions can be used for continuous data; 8.6 Probability = area; 8.7 To calculate probability, start by finding f(x).; 8.8 . then find probability by finding the area; 8.

9 We''ve found the probability; 8.10 Searching for a soul sole mate; 8.11 Male modelling; 8.12 The normal distribution is an "ideal" model for continuous data; 8.13 So how do we find normal probabilities?; 8.14 Three steps to calculating normal probabilities; 8.15 Step 1: Determine your distribution; 8.16 Step 2: Standardize to N(0, 1); 8.

17 To standardize, first move the mean.; 8.18 . then squash the width; 8.19 Now find Z for the specific value you want to find probability for; 8.20 Step 3: Look up the probability in your handy table; 8.21 Julie''s probability is in the table; 8.22 And they all lived happily ever after;Chapter 9: Using the Normal Distribution ii: Beyond Normal; 9.

1 Love is a roller coaster; 9.2 All aboard the Love Train; 9.3 Normal bride + normal groom; 9.4 It''s still just weight; 9.5 How''s the combined weight distributed?; 9.6 Finding probabilities; 9.7 More people want the Love Train; 9.8 Linear transforms describe underlying changes in values.

; 9.9 .and independent observations describe how many values you have; 9.10 Expectation and variance for independent observations; 9.11 Should we play, or walk away?; 9.12 Normal distribution to the rescue; 9.13 When to approximate the binomial distribution with the normal; 9.14 Revisiting the normal approximation; 9.

15 The binomial is discrete, but the normal is continuous; 9.16 Apply a continuity correction before calculating the approximation; 9.17 All aboard the Love Train; 9.18 When to approximate the binomial distribution with the normal; 9.19 A runaway success!;Chapter 10: Using Statistical Sampling: Taking Samples; 10.1 The Mighty Gumball taste test; 10.2 They''re running out of gumballs; 10.3 Test a gumball sample, not the whole gumball population; 10.

4 How sampling works; 10.5 When sampling goes wrong; 10.6 How to design a sample; 10.7 Define your sampling frame; 10.8 Sometimes samples can be biased; 10.9 Sources of bias; 10.10 How to choose your sample; 10.11 Simple random sampling; 10.

12 How to choose a simple random sample; 10.13 There are other types of sampling; 10.14 We can use stratified sampling.; 10.15 .or we can use cluster sampling.; 10.16 .

or even systematic sampling; 10.17 Mighty Gumball has a sample;Chapter 11: Estimating Populations and Samples: Making Predictions; 11.1 So how long does flavor really last for?; 11.2 Let''s start by estimating the population mean; 11.3 Point estimators can approximate population parameters; 11.4 Let''s estimate the population variance; 11.5 We need a different point estimator than sample variance; 11.6 Which formula''s which?; 11.

7 Mighty Gumball has done more sampling; 11.8 It''s a question of proportion; 11.9 Buy your gumballs here!; 11.10 So how does this relate to sampling?; 11.11 The sampling.