Specify the population parameter of interest. Step 2. Formulate the appropriate null and alternative hypotheses Step 3. Specify the level of significance. Step 4. Compute the correlation coefficient and the test statistic. Step 5. Construct the rejection region and decision rule. Step 6. Reach a decision Step 7. Draw a conclusion.
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.
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The following sample data were obtained from companies to determine should the number of stocks a company have depends on the rate of return. Is it reasonable to conclude that there is a position association in the population between the number of stocks and rate of return by using a .05 significant level. MUCH SHOW YOUR WORK FOR FULL CREDIT.
|
Companies |
# of Stocks |
Rate of Return |
|
A |
8 |
.15 |
|
B |
16 |
.16 |
|
C |
25 |
.21 |
|
D |
16 |
.18 |
|
E |
20 |
.18 |
|
F |
16 |
.19 |
|
G |
20 |
.15 |
|
H |
20 |
.17 |
|
I |
16 |
.13 |
|
J |
10 |
.11 |
Step 1. Specify the population parameter of interest.
Step 2. Formulate the appropriate null and alternative hypotheses
Step 3. Specify the level of significance.
Step 4. Compute the
Step 5. Construct the rejection region and decision rule.
Step 6. Reach a decision
Step 7. Draw a conclusion.
-
The following sample data were obtained from companies to determine should the number of stocks a company have depends on the rate of return. Is it reasonable to conclude that there is a position association in the population between the number of stocks and rate of return by using a .05 significant level. MUCH SHOW YOUR WORK FOR FULL CREDIT.
|
Companies |
# of Stocks |
Rate of Return |
|
A |
8 |
.15 |
|
B |
16 |
.16 |
|
C |
25 |
.21 |
|
D |
16 |
.18 |
|
E |
20 |
.18 |
|
F |
16 |
.19 |
|
G |
20 |
.15 |
|
H |
20 |
.17 |
|
I |
16 |
.13 |
|
J |
10 |
.11 |
Step 1. Specify the population parameter of interest.
Step 2. Formulate the appropriate null and alternative hypotheses
Step 3. Specify the level of significance.
Step 4. Compute the correlation coefficient and the test statistic.
Step 5. Construct the rejection region and decision rule.
Step 6. Reach a decision
Step 7. Draw a conclusion.
-
The following sample data were obtained from companies to determine should the number of stocks a company have depends on the rate of return. Is it reasonable to conclude that there is a position association in the population between the number of stocks and rate of return by using a .05 significant level. MUCH SHOW YOUR WORK FOR FULL CREDIT.
|
Companies |
# of Stocks |
Rate of Return |
|
A |
8 |
.15 |
|
B |
16 |
.16 |
|
C |
25 |
.21 |
|
D |
16 |
.18 |
|
E |
20 |
.18 |
|
F |
16 |
.19 |
|
G |
20 |
.15 |
|
H |
20 |
.17 |
|
I |
16 |
.13 |
|
J |
10 |
.11 |
Step 1. Specify the population parameter of interest.
Step 2. Formulate the appropriate null and alternative hypotheses
Step 3. Specify the level of significance.
Step 4. Compute the correlation coefficient and the test statistic.
Step 5. Construct the rejection region and decision rule.
Step 6. Reach a decision
Step 7. Draw a conclusion.
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