Intermediate Algebra Tutorial
Carefully work through the problems. These questions are repeated on the preparation quiz for this lesson. This is not designed to be a comprehensive review. There may be items on the exam that are not covered in this review. Similarly, there may be items in this review that are not tested on this exam.
You are strongly encouraged to review the readings, homework exercises, and other activities from Units as you prepare for the exam. Use the Index to review definitions of important terms.
You should be able to do: Interpret the overall pattern in a scatter plot to assess linearity and direction. Calculate the correlation coefficient.
Identify the explanatory and response variable in a study. Calculate the slope and intercept of a regression model. Interpret the slope of the regression model. Make predictions using a regression model Confidence Intervals for the slope of the regression line: Calculate and interpret a confidence interval for the slope of the regression line given a confidence level.
Identify a point estimate and margin of error for the confidence interval. Show the appropriate connections between the numerical and graphical summaries that support the confidence interval. Check the requirements for the confidence interval. Hypothesis Testing for the slope of the regression line: State the null and alternative hypothesis.
Calculate the test-statistic, degrees of freedom and p-value of the hypothesis test.
Check the requirements for the hypothesis test. Show the appropriate connections between the numerical and graphical summaries that support the hypothesis test.
Draw a correct conclusion for the hypothesis test. Here are the summaries for each lesson in unit 4. Reviewing these key points from each lesson will help you in your preparation for the exam. Lesson 21 Recap Creating scatterplots of bivariate data allows us to visualize the data by helping us understand its shape linear or nonlineardirection positive, negative, or neitherand strength strong, moderate, or weak.
The covariance is a measure of how two variables vary together. For example, in the correlation between number of powerboats and number of manatee deaths, the number of deaths is affected by the number of powerboats in the water, but not the other way around.
It can be expressed as: We conduct a hypothesis test on bivariate data to know if there is a linear relationship between the two variables.
The appropriate hypotheses for this test are: I met with my group and completed the Group Quiz with them. Mark all that apply a. The number of people in a randomly selected classroom on campus b.4A.
Mathematical Models. The chapter intro talks about points following a “linear model”. But what is a linear model, and what does it mean to follow one? Well, since a linear model is one kind of mathematical model, let’s talk a little bit about mathematical models.
Standard linear regression models with standard estimation techniques make a number of assumptions about the predictor variables, .
Standard Form and Intercepts Algebra On a graph, the x-intercept is where the line crosses the x-axis. The y-intercept is where a line crosses the y-axis. Practice: Look at the graphs below and give the coordinates of the x and y-intercepts.
The following questions are intended to help you judge your preparation for the final exam. Carefully work through the problems. These questions are repeated on the preparation quiz for this lesson. Simply knowing how to take a linear equation and graph it is only half of the battle.
You should also be able to come up with the equation if you're given the right information.
This is called the slope-intercept form because "m" is the slope and "b" gives the y-intercept. (For a review of how this equation is used for graphing, look at slope and graphing.).
I like slope-intercept form the best.