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Chapters 1 - 4 The Role of Statistics & Graphical Methods for Describing Data. Statistics. the science of collecting, analyzing, and drawing conclusions from data. Descriptive statistics. the methods of organizing & summarizing data.
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Chapters 1 - 4The Role of Statistics&Graphical Methods for Describing Data
Statistics the science of collecting, analyzing, and drawing conclusions from data
Descriptive statistics the methods of organizing & summarizing data If the sample of high school GPAs contained 10,000 numbers, how could the data be described or summarized? • Create a graph • State the range of GPAs • Calculate the average GPA
Inferential statistics involves making generalizations from a sample to a population Based on the sample, if the average GPA for high school graduates was 3.0, what generalization could be made? Be sure to sample from the population of interest!! The average national GPA for this year’s high school graduate is approximately 3.0. Could someone claim that the average GPA for CFBISD graduates is 3.0? No. Generalizations based on the results of a sample can only be made back to the population from which the sample came from.
Variable any characteristic whose value may change from one individual to another Is this a variable . . . The number of wrecks per week at the intersection outside?
Data observations on single variable or simultaneously on two or more variables For this variable . . . The number of wrecks per week at the intersection outside . . . What could observations be?
Categorical variables • or qualitative • identifies basic differentiating characteristics of the population
Numerical variables • or quantitative • observations or measurements take on numerical values • makes sense to average these values • two types - discrete & continuous
Discrete (numerical) • listable set of values • usually counts of items
Continuous (numerical) • data can take on any values in the domain of the variable • usually measurements of something
Classification by the number of variables • Univariate - data that describes a single characteristic of the population • Bivariate - data that describes two characteristics of the population • Multivariate - data that describes more than two characteristics (beyond the scope of this course
the appraised value of homes in Carrollton the color of cars in the teacher’s lot the number of calculators owned by students at your school the zip code of an individual the amount of time it takes students to drive to school Identify the following variables: Discrete numerical Categorical Discrete numerical Categorical Continuous numerical
Bar Graph • Used for categorical data • Bars do not touch • Categorical variable is typically on the horizontal axis • To describe – comment on which occurred the most often or least often • May make a double bar graph or segmented bar graph for bivariate categorical data sets
Pie (Circle) graph • Used for categorical data • To make: • Proportion 360° • Using a protractor, mark off each part • To describe – comment on which occurred the most often or least often
Dotplot • Used with numerical data (either discrete or continuous) • Made by putting dots (or X’s) on a number line • Can make comparative dotplots by using the same axis for multiple groups
Symmetrical • refers to data in which both sides are (more or less) the same when the graph is folded vertically down the middle • bell-shaped is a special type • has a center mound with two sloping tails
Uniform • refers to data in which every class has equal or approximately equal frequency
Skewed (left or right) • refers to data in which one side (tail) is longer than the other side • the direction of skewness is on the side of the longer tail
Bimodal (multi-modal) • refers to data in which two (or more) classes have the largest frequency & are separated by at least one other class
1. Center • discuss where the middle of the data falls • three types of central tendency • mean, median, & mode
2. Spread • discuss how spread out the data is • refers to the variability of the data • Range, standard deviation, IQR
3. Shape • refers to the overall shape of the distribution • symmetrical, uniform, skewed, or bimodal
4. Unusual occurrences • outliers - value that lies away from the rest of the data • gaps • clusters • anything else unusual