stat exam

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http://www.straighterline.com/mat202xxsaysl001000001-b.html 

 

Unit

Unit Title

Subunit Title

Objectives

1

Statistics and Data

  • The Science of Statistics and Its Importance
  • Methods for Describing Data
  • Apply various types of sampling methods to data collection;
  • Create and interpret frequency tables;
  • display data graphically and interpret the following types of graphs: stem plots, histograms, and boxplots;
  • identify, describe, and calculate the following measures of the location of data: quartiles and percentiles;
  • identify, describe, and calculate the measures of the center of mean, median, and mode; and
  • Identify, describe, and calculate the following measures of the spread of data: variance, standard deviation, and range.

2

Elements of Probability and Random Variables  

  • Classical Probability Model
  • Random Variables
  • Understand and use the terminology of probability;
  • Determine whether two events are mutually exclusive and whether two events are independent;
  • Calculate probabilities using the addition Rules and multiplication rules;
  • Construct and interpret Venn diagrams;
  • Apply useful counting rules in the context of combinational probability;
  • Identify and use common discrete probability distribution functions;
  • Calculate and interpret expected values;
  • Identify the binomial probability distribution, and apply it appropriately;
  • Identify the Poisson probability distribution, and apply it appropriately.

3

Normal Distributions and Sampling Distributions

  • Normal Distributions
  • The Concept of Sampling Distributions
  • Sampling Distributions for Common Statistics  
  • Identify and use continuous probability density functions;
  • Identify the normal probability distribution, and apply it appropriately;
  • Apply the central theorem to approximate sampling distributions;
  • Describe the role of sampling distributions in inferential statistics;
  • Interpret and create graphs of a probability distribution for the mean and a discrete variable;
  • Describe a sampling distribution in terms of repeated sampling;
  • Compute the mean and standard deviation of the sampling distribution of the population;
  • Identify or approximate a sampling distribution based on the properties of the population;
  • Compare and evaluate the sampling distributions of different sample sizes; and
  • Compare and evaluate the performance of different estimators based on their sampling distributions.

4

Estimation with Confidence Intervals

  • Point Estimators and Their Characteristics
  • Confidence Intervals
  • Explain the central limit theorem, and use it to construct confidence intervals;
  • Compare t-distribution and normal distribution;
  • Apply and interpret the central limit theorem for sample averages;
  • Calculate and interpret confidence intervals for population averages and one population proportions; and
  • Interpret the student-t probability distribution as the sample size changes.

5

Hypothesis Test

  • Elements of Hypothesis Testing
  • Tests of Population Means  
  • Differentiate between type I and type II errors;
  • Describe hypothesis testing in general and in practice;
  • Interpret and explain how to conduct hypothesis tests for a single population mean and population proportion, when the population standard deviation is unknown;
  • Interpret and explain how to conduct hypothesis tests for a single population proportion; and
  • Classify hypothesis tests by type.

6

Linear Regression  

  • The Regression Model
  • Fitting the Model
  • Discuss basic ideas of linear regression and correlation;
  • Identify the assumptions that inferential statistics in regression are based on;
  • Compute the standard error of a slope;
  • Test a slope for significance;
  • Construct a confidence interval on a slope; and
  • Calculate and interpret the correlation coefficient.

7

Review

  • Review        
  • Review and Final Exam

 

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