Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Review Topics for Final Exam
Document related concepts
Transcript
Statistics 2014, Summer 2013 Final Exam Review Topics Chapter 1 – Data Collection Statistics, Population, Sample, Parameter, Statistic, Variable, Data Branches of statistics: Descriptive, Inferential Types of data: 1) Attribute, or qualitative 2) Numerical, or quantitative (discrete or continuous) Representative sample v. census Simple random sample of size n; how to select such a sample Sampling error v. nonsampling error Designed experiment, experimental unit, experimental treatment, response variable Chapter 2 – Organizing and Summarizing Data Categorical frequency distribution Grouped frequency distribution: Class limits, class width, cumulative frequency, relative frequency, cumulative relative frequency Histogram, used with quantitative data – Distribution shapes Pareto chart (bar graph), pie graph – both used with qualitative data Time series plot Chapter 3 – Numerically Summarizing Data Measures of Central Tendency: Mean, Median, Mode; properties of Mean, Median, Mode Distribution shapes and relationships among measures of central tendency Which measure of central tendency is preferred, depending on shape of distribution and type of data. Measures of Variability: 1) Range, not the most useful; 2) Variance, more useful; 3) Standard Deviation, most useful (why?) Empirical Rule Measures of Position z-score, used for comparing scores from different data sets percentiles, locates position of a score relative to the rest of the data set quartiles and interquartile range 5-number summary of a data set; outliers Boxplots (box-and-whisker plots), information obtained from boxplot Chapter 4 – Describing the Relation between Two Variable Scatterplot to look for linear trend relationship, types of trends Pearson correlation coefficient to measure direction and strength of linear trend, properties of r Regression equation and line of best fit to the data – predicting value of dependent variable for a given value of the independent variable, interpreting the slope and intercept Chapter 5 – Probability Random experiment, and sample space Events: Simple event, Compound event. Assigning probabilities to events: a) Classical approach, b) Relative frequency (empirical) approach Interpreting a probability Mutually exclusive events Complement of an event – Complement Rule Unions and intersections of events Basic Laws of Probability: (Kolmogorov’s Axioms): 1) For any event A, 0 P(A) 1. 2) P(S) = 1. In other words, the outcome of the random experiment is certain to be in the sample space. 3) If two events A and B are mutually exclusive, then P( A B) P( A) P( B) . Addition Rule for Non-Mutually Exclusive Events Conditional Probability Independent events and Multiplication Rule for independent events Chapter 6 – Probability Distributions Random variables, discrete and continuous Probability distribution Required Properties of a Discrete Probability Distribution Expectation, or mean, of a probability distribution of a discrete random variable X. Interpreting the mean. Variance of a discrete random variable X Conditions for a binomial experiment Binomial probability distribution; binomial random variable X Finding binomial probabilities using the TI-83 Mean, variance and standard deviation for the Binomial Distribution Chapter 7 – The Normal Probability Distribution Characteristics of normal distributions The Empirical Rule: Standard Normal Distribution Finding normal probabilities using the TI-83 calculator Inverting the process: finding values of x (or z) corresponding to a particular probability Chapter 8 – Sampling Distributions and the Central Limit Theorem Sampling distribution of the sample mean Central Limit Theorem; finding approximate probabilities for the sample mean Sampling distribution of the sample proportion Finding approximate probabilities for the sample proportion Chapter 9 – Confidence Intervals and Sample Size Point estimator of a parameter Confidence interval estimate: 1) Point estimate, 2) Width of interval, 3) Level of confidence How to find confidence interval for a population mean. How to find confidence interval for a population proportion.
