Question
In: Math
Consider a possible linear relationship between two variables that you would like to explore Define the...
Consider a possible linear relationship between two variables that you would like to explore
Define the relationship of interest and a data collection technique.
Determine the appropriate sample size and collect the data.
Perform the appropriate analysis to determine if there is a statistically significant linear relationship between the two variables.
Describe the relationship in terms of strength and direction.
Construct a model of the relationship and evaluate the validity of that model.
Solutions
milcah answered 8 months agoRelated Solutions
Consider a possible linear relationship between two variables that you would like to explore. 1) Define...
Consider a possible linear relationship between two variables
that you would like to explore.
1) Define the relationship of interest and a data collection
technique.
2) Determine the appropriate sample size and collect the
data.
3) Perform the appropriate analysis to determine if there is a
statistically significant linear relationship between the two
variables. Describe the relationship in terms of strength and
direction.
4) Construct a model of the relationship and evaluate the
validity of that model.
Provide complete sentence...
You are testing the null hypothesis that there is no linear relationship between two variables, X...
You are testing the null hypothesis that there is no linear
relationship between two variables, X and Y. From your sample of n
equals 18, you determine that b1=4.2 and S Subscript b 1=1.1.
a. What is the value of t Subscript STAT?
b. At the alpha=0.05 level of significance, what are the
critical values?
c. Based on your answers to (a) and (b), what statistical
decision should you make?
d. Construct a 95% confidence interval estimate of the
population...
You are testing the null hypothesis that there is no linear relationship between two variables, X...
You are testing the null hypothesis that there is no linear
relationship between two variables, X and Y. From your sample of n
=18, you determine that b1=4.2 and Sb1equals=1.2
a. What is the value of tSTAT?
b. At the alphaα=0.05 level of significance, what are the
critical values?
c. Based on your answers to (a) and (b), what statistical
decision should you make?
d. Construct a 95% confidence interval estimate of the
population slope, betaβ1.
You are testing the null hypothesis that there is no linear relationship between two variables, X...
You are testing the null hypothesis that there is no linear
relationship between two variables, X and Y. From your sample of
n=18, you determine that b1=4.4 and Sb1=1.3
a. What is the value of t stat?
b. At the a=0.05 level of significance, what are the critical
values?
c. Based on your answers to (a) and (b), what statistical
decision should you make?
d. Construct a 95% confidence interval estimate of the
population slope,β1.
A measure of the strength of the linear relationship that exists between two variables is called:...
A measure of the strength of the linear relationship that exists
between two variables is called: Slope/Intercept/Correlation
coefficient/Regression equation. If both variables X and Y increase
simultaneously, then the coefficient of correlation will be:
Positive/Negative/Zero/One. If the points on the scatter diagram
indicate that as one variable increases the other variable tends to
decrease the value of r will be: Perfect positive/Perfect
negative/Negative/Zero. The range of correlation coefficient is: -1
to +1/0 to 1/-∞ to +∞/0 to ∞. Which of...
1. The linear correlation coefficient r measures of the linear relationship between two variables. (a) Distance...
1. The linear correlation coefficient r measures of the linear
relationship between two variables. (a) Distance (b) size (c)
strength (d) direction 2. 10 pairs of sample data were obtained
from a study which looked at household income and the number of
people in the household who smoked (cigarettes). The value of the
linear correlation coefficient r was computed and a result of -
0.989 was obtained. All of the following (below) are conclusions
that can be drawn from the...
1. The linear correlation coefficient r measures of the linear relationship between two variables. (a) Distance...
1. The linear correlation coefficient r measures of the linear
relationship between two variables. (a) Distance (b) size (c)
strength (d) direction 2. 10 pairs of sample data were obtained
from a study which looked at household income and the number of
people in the household who smoked (cigarettes). The value of the
linear correlation coefficient r was computed and a result of -
0.989 was obtained. All of the following (below) are conclusions
that can be drawn from the...
1. The linear correlation coefficient r measures of the linear relationship between two variables. (a) Distance...
1. The linear correlation coefficient r measures of the linear
relationship between two variables. (a) Distance (b) size (c)
strength (d) direction 2. 10 pairs of sample data were obtained
from a study which looked at household income and the number of
people in the household who smoked (cigarettes). The value of the
linear correlation coefficient r was computed and a result of -
0.989 was obtained. All of the following (below) are conclusions
that can be drawn from the...
1. The linear correlation coefficient r measures of the linear relationship between two variables. (a) Distance...
1. The linear correlation coefficient r measures of the linear
relationship between two variables. (a) Distance (b) size (c)
strength (d) direction 2. 10 pairs of sample data were obtained
from a study which looked at household income and the number of
people in the household who smoked (cigarettes). The value of the
linear correlation coefficient r was computed and a result of -
0.989 was obtained. All of the following (below) are conclusions
that can be drawn from the...
A company would like to examine the linear relationship between the age and credit score of...
A company would like to examine the linear relationship between
the age and credit score of an individual. The following table
shows the credit scores and ages of 5 randomly selected people.
These data have a sample correlation coefficient, rounded to three
decimal places, of
0.973.
Using this data and
α=0.10,
test if the population correlation coefficient between a
person's age and credit score is different than zero. What
conclusions can you draw?
Age
32
24
52
21
34
Credit...
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