MATH 2050 - Calculus-Based Prob/Stats

3 sem hrs cr

This course is an introduction to probability and statistics. Data analysis, probability, and statistical inference are introduced in this course. The inference material covers means, proportions, and variances for one and two samples, one-way ANOVA, regression and correlation, and chi-square analysis. Prerequisite: MATH 1830  or MATH 1910  

In rare and unusual circumstances, a course prerequisite can be overridden with the permission of the Department Lead for the discipline.

Formerly/Same As  

Upon successful completion of this course, students will be able to…

• distinguish between descriptive and inferential statistics.
• construct and graph a frequency distribution as a histogram, a frequency polygon, and pie chart.
• calculate measures of central tendency.
• calculate measures of variance.
• utilize the concepts of union and intersection in probability experiments, sample spaces, and events.
• find the probability of an event.
• apply properties of probabilities.
• use counting techniques in probability.
• apply properties of conditional probability and independent events.
• utilize the properties of the binomial distribution.
• find the z-score.
• utilize the z-score when finding probabilities and continuous variables.
• algebraically find the score when given a probability.
• utilize the normal curve to approximate the binomial distribution.
• utilize the central limit theorem to find the probabilities and sample means.
• test hypotheses about population parameters.
• utilize the t-test when the normal curve is unsuitable.
• construct and utilize confidence intervals.
• calculate appropriate sample sizes for tests of proportions and means.
• test hypotheses involving multinomial experiments and contingency tables.
• utilize the Chi-Square distribution with studies involving variance and standard deviation.
• compare two or more population means by parametric and nonparametric models.
• determine the appropriate sample size to estimate the difference between a pair of means.
• utilize the analysis of variance (ANOVA) to compare two or more populations.
• compare two or more population proportions by parametric and nonparametric methods.
• determine the appropriate sample size required to compare two population proportions.
• determine linear correlation by using parametric and nonparametric methods.
• calculate coefficient of correlation and coefficient of determination.
• interpret the y-intercept, slope, and standard deviation of the linear regression model.