A student is interested in the relation between ?, the number of job changes and ?, the annual salary (in thousands of dollars) for people living in the Columbus area. A random sample of 10 people employed in Columbus provided the following information: ? 4 7 5 6 1 5 9 10 10 3 ? 33 37 34 32 32 38 43 37 40 33 a) The regression equation is: _____ b) Graph the data and the regression equation on the same graph. Label the graph. c) Describe the apparent relationship between the number of job changes and annual salary. d) What does the slope of the regression equation represent in terms of the annual salary? e) Identify any outliers or potential influential observations. Explain your reasoning. f) Identify the predictor and response variables. g) r 2= _____ h) r= _____ i) Interpret the meaning of r2 and how useful the regression equation is for making predictions. j) Interpret the meaning of r in terms of the linear relationship between the number of job changes and annual salary. k) Use the regression equation to predict the annual salary of a randomly selected employee who has had 8 job changes. l) A particular employee who has had 8 job changes has an annual salary of $35,000. Calculate the residual for this data employee, to the nearest cent. m) Give a short interpretation of the y-intercept of the regression line in the context of the problem.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
A student is interested in the relation between ?,
the number of job changes and ?, the annual salary
(in thousands of dollars) for people living in the
Columbus area. A random sample of 10 people
employed in Columbus provided the following
information:
? 4 7 5 6 1 5 9 10 10 3
? 33 37 34 32 32 38 43 37 40 33
a) The regression equation is: _____
b) Graph the data and the regression equation on the
same graph. Label the graph.
c) Describe the apparent relationship between the
number of job changes and annual salary.
d) What does the slope of the regression equation
represent in terms of the annual salary?
e) Identify any outliers or potential influential
observations. Explain your reasoning.
f) Identify the predictor and response variables.
g) r
2= _____
h) r= _____
i) Interpret the meaning of r2 and how useful the
regression equation is for making predictions.
j) Interpret the meaning of r in terms of the linear
relationship between the number of job changes
and annual salary.
k) Use the regression equation to predict the annual
salary of a randomly selected employee who has
had 8 job changes.
l) A particular employee who has had 8 job changes
has an annual salary of $35,000. Calculate the
residual for this data employee, to the nearest cent.
m) Give a short interpretation of the y-intercept of the
regression line in the context of the problem.
Could you please explain how to know what test should be used and how to solve it using a Ti84
Trending now
This is a popular solution!
Learn your way
Includes step-by-step video
Step by step
Solved in 4 steps with 2 images
