MA 122 - Introduction to Statistics

Objectives

1. Understand the difference between descriptive and inferential statistics.
2. Know the uses and possible abuses of statistics.
3. Have an overview of what the course is about.

1. Know how to construct pictures of data such as histograms, frequency polygons, ogives and pie charts.
2. Understand and compute various measures of central tendency such as mean, median, mode, mid-range, and weighted mean.
3. Understand and compute various measures of dispersion such as range, variance and standard deviation.

1. Understand the difference between empirical and classical probability.
2. Understand the fundamentals of classical probability theory.

1. Understand the concept of discrete and random variables.
2. Know how to compute the mean and variance for a discrete probability distribution.
3. Understand the binomial distribution and how it is used in binomial experiments.
4. Know how to compute the mean and the variance for a binomial distribution.
5. Recognize the difference and properties of various distribution shapes (bell-shaped, symmetrical and non-symmetrical).

1. Understand the properties of the standard normal distribution and non-standard normal distribution.
2. Understand the concept of standard scores.
3. Using knowledge of the normal distribution to approximate the binomial distribution.
4. Understand and make use of the Central Limit Theorem.

Know how to perform the following one-sample hypotheses tests for:
1. The mean (large and small samples)
2. proportions
3. variances and/or standard deviations

Know how to compute confidence intervals for the following:
1. Means
2. Proportions
3. Variances and/or standard deviations

Know how to perform the following two-sample hypothesis tests for:
1. Variances and/or standard deviations
2. Means
3. Proportions

1. Understand the concept pr linear correlation
2. Understand the concept of linear regression
3. Know how to perform linear correlation and regression analysis.

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Syllabus

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Outline

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Text

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Examples of Objectives