1. UNIT #1 CHAPTERS BY JEREMY GREEN, ADAM PAQUETTEY, AND MATT STAUB

2. CHAPTER 2: DATA THE FIVE W’S WHO- THE ROWS OF A DATA TABLE THAT CORRESPOND TO THE INDIVIDUAL CASES ABOUT WHOM WE RECORD SOME STATISTICS. WHAT- THE CHARACTERISTICS RECORDED ABOUT EACH INDIVIDUAL WHY- REASON DATA WAS COLLECTED WHERE- WHERE THE DATA WAS COLLECTED WHEN- THE TIME THAT THE DATA WAS COLLECTED HOW- IT IS IMPORTANT TO EXPLAIN THE TYPE OF EXPERIMENT, SURVEY, OR STUDY THAT WAS CONDUCTED COLLECTED DATA IS ORGANIZED INTO DATA TABLES XYZ A1233456756789 B1233457890987654

3. CHAPTER 2 CONT. VARIABLES MEASURED IN UNITS CATEGORICAL VARIABLE- ANSWERS QUESTIONS ABOUT HOW CASES FALL INTO CATEGORIES QUANTITATIVE VARIABLE- ANSWERS QUESTIONS ABOUT THE QUANTITY OF WHAT IS MEASURED TYPES OF RESPONDENTS SUBJECTS- PEOPLE WE EXPERIMENT ON EXPERIMENTAL UNITS- ANIMALS, PLANTS, WEB SITES AND OTHER INANIMATE SUBJECTS RESPONDENTS- INDIVIDUALS WHO ANSWER A SURVEY

4. CHAPTER 3: DISPLAYING AND DESCRIBING DATA THREE RULES OF DATA ANALYSIS 1. MAKE A PICTURE 2. MAKE A PICTURE 3. MAKE A PICTURE FREQUENCY TABLE TABLE THAT ORGANIZES COUNTS FOR CATEGORICAL DATA RELATIVE FREQUENCY TABLES SHOW PERCENTS IMPORTANT TO KNOW PROPORTIONS SO WE CAN USE PERCENTS AREA PRINCIPLE- THE AREA OCCUPIED BY A PART OF THE GRAPH SHOULD CORRESPOND TO THE MAGNITUDE OF THE VALUE IT REPRESENTS.

5. CHAPTER 3 CONT. BAR CHART- DISPLAYS THE DISTRIBUTION OF A CATEGORICAL VARIABLE, SHOWING THE COUNTS FOR EACH CATEGORY NEXT TO EACH OTHER FOR EASY COMPARISON. PIE CHARTS- SHOWS ALL THE CASES ON AS A CIRCLE AND THEY SLICE THE CIRCLE INTO PIECES WHO SIZES ARE PROPORTIONAL TO THE FRACTION OF THE WHOLE OF EACH CATEGORY. CONTINGENCY TABLE SHOWS TWO VARIABLES SIDE BY SIDE MARGINAL DISTRIBUTION- SHOWS THE COUNTS FOR EACH VARIABLE CONDITIONAL DISTRIBUTION- SHOWS THE PERCENTS FOR EACH VARIABLE INDEPENDENCE- WHEN THE DISTRIBUTION OF ONE VARIABLE IS THE SAME FOR ALL CATEGORIES OF ANOTHER

6. XY A5678234567 B98765345678 Total9999998765 Bar ChartPie Chart Contingency Table

7. CHAPTER 4: DISPLAYING AND SUMMARIZING DATA HISTOGRAM- REPRESENTS COUNTS AS BARS AND PLOTS THEM AGAINST QUANTITATIVE DATA. RELATIVE FREQUENCY HISTOGRAM- SAME AS HISTOGRAM, REPLACING THE COUNTS ON THE VERTICAL AXIS WITH PERCENTAGES OF THE TOTAL NUMBER OF CASES. STEM-AND-LEAF PLOT- SIMILAR TO A HISTOGRAM, BUT IT SHOWS EACH INDIVIDUAL VALUE. DOTPLOT- A DOT IS PLACED ALONG AN AXIS FOR EACH CASE IN THE DATA. QUANTITATIVE DATA CONDITION- THE DATA ARE VALUES OF A QUANTITATIVE VARIABLE WHOSE UNITS ARE KNOWN. MUST KNOW THIS BEFORE MAKING A GRAPHICAL DISPLAY.

8. CHAPTER 4 CONT. THREE THINGS TO DESCRIBE A DISTRIBUTION 1. SHAPE- WHETHER IT UNIMODAL OR BIMODAL, SYMMETRIC OR SKEWED, AND WHETHER OR NOT THERE ARE OUTLIERS. 2. CENTER- THE CENTER OF THE DATA. USUALLY TALKS ABOUT THE MEDIAN.  MEDIAN-IS THE MIDDLE VALUE THAT DIVIDES THE TWO HALVES OF THE HISTOGRAM. 3. SPREAD- THE RANGE AND INTERQUARTILE RANGE OF THE DATA.  RANGE- THE DIFFERENCE BETWEEN THE MAXIMUM AND THE MINIMUM OF THE DATA.  INTERQUARTILE RANGE- THE DIFFERENCE BETWEEN THE UPPER QUARTILE RANGE AND THE LOWER QUARTILE RANGE 5 NUMBER SUMMARY- REPORTS THE MEDIAN, QUARTILES, MINIMUM, AND THE MAXIMUM OF A DATA SET.

9. CHAPTER 4 CONT. MEAN FEELS LIKE THE CENTER BECAUSE IT IS THE POINT WHERE THE HISTOGRAM BALANCES. CALCULATED BY DIVIDING THE TOTAL OF YOUR DATA BY THE NUMBER OF DATA POINTS. USED WHEN THE HISTOGRAM IS SYMMETRIC AND THERE ARE NO OUTLIERS. MEDIAN IS RESISTANT TO VALUES THAT ARE EXTRAORDINARILY LARGE OR SMALL USED WHEN THE DATA IS SKEWED OR HAS OUTLIERS. STANDARD DEVIATION ACCOUNTS FOR HOW FAR EACH VALUE IS FROM THE MEAN. ONLY WORKS FOR SYMMETRIC DATA. CANNOT BE CALCULATED BY ITS SELF, SO YOU MUST TAKE THE SQUARE ROOT OF THE VARIANCE IN ORDER TO OBTAIN THE STANDARD DEVIATION.

10. CHAPTER 5: UNDERSTANDING AND COMPARING DISTRIBUTIONS BOXPLOT- A GRAPHICAL REPRESENTATION OF A 5 NUMBER SUMMARY. ALSO, SHOWS OUTLIERS OF THE DATA. OUTLIERS ANY POINT THAT HAS LEVERAGE ON THE DATA DUE TO BEING EXTREMELY HIGH OR EXTREMELY LOW. TO DETERMINE WHETHER OR NOT A POINT IS AN OUTLIER YOU USE THE FORMULA: 1.5 X IQR THEN SUBTRACT FROM LOWER QUARTILE AND ADD TO UPPER QUARTILE. RE-EXPRESSING OR TRANSFORMING DATA- APPLY A SIMPLE FUNCTION TO FIX SKEWED DATA. EX: TAKING THE NATURAL LOG OF YOUR DATA. BOXPLOTS ALLOW YOU TO COMPARE MULTIPLE SPREADS OF DATA.

11. COMPARING DISTRIBUTIONS

12. CHAPTER 6: THE STANDARD DEVIATION AS A RULER AND THE NORMAL MODEL STANDARD DEVIATION ANSWERS THE QUESTION HOW FAR IS THIS VALUE FROM THE MEAN AND HOW DIFFERENT ARE THESE TWO STATISTICS STANDARDIZED VALUES OR Z-SCORES MEASURE THE DISTANCE OF EACH DATA VALUE FROM THE MEAN IN STANDARD DEVIATIONS. STANDARDIZED VALUES HAVE NO UNITS. SHIFTING DATA WHEN WE ADD OR SUBTRACT A CONSTANT TO EACH VALUE ALL MEASURES OF POSITION(CENTER, PERCENTILES, MIN, AND MAX) WILL INCREASE OR DECREASE BY THAT SAME CONSTANT. THIS LEAVES SPREAD THE SAME. WHEN WE MULTIPLY OR DIVIDE BY A CONSTANT TO EACH VALUE ALL MEASURES OF POSITION AND SPREAD WILL BE MULTIPLIED OR DIVIDED BY THAT CONSTANT.

13. CHAPTER 6 CONT. NORMAL MODEL THE BELL SHAPE CURVE THAT IT IS APPROPRIATE FOR DISTRIBUTIONS WHOSE SHAPES ARE UNIMODAL AND SYMMETRIC. NUMBERS WE USE TO SPECIFY THIS MODEL ARE CALLED PARAMETERS. SUMMARIES OF THIS DATA ARE CALLED STATISTICS. A NORMAL MODEL WITH A MEAN OF 0 AND A STANDARD DEVIATION OF 1 IS CALLED THE STANDARD NORMAL MODEL. IN ORDER TO USE THIS MODEL THE DATA MUST MEET THE NEARLY NORMAL CONDITION. THE 68-95-99.7 RULE- SAYS THAT 68% OF THE DATA WILL FALL WITHIN 1 STANDARD DEVIATION OF THE MEAN, 95% WILL FALL WITHIN 2 STANDARD DEVIATIONS, AND 99.7% WILL FALL WITHIN 3 STANDARD DEVIATIONS.

14. CHAPTER 6 CONT. RULES FOR WORKING WITH THE NORMAL MODEL 1. MAKE A PICTURE 2. MAKE A PICTURE 3. MAKE A PICTURE NORMAL PROBABILITY PLOT- TELLS YOU IF YOUR DATA IS NORMAL BY SHOWING WHETHER OR NOT YOUR DATA LIES ON A DIAGONAL LINE.

15. NORMAL MODEL AND NORMAL PROBABILITY PLOT