The weights (in pounds) of 6 vehicles and the variability of their braking distances (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface? Use α=0.01. Weight, x 5930 5390 6500 5100 5890 4800 Variability in braking distance, y 1.76 1.95 1.91 1.62 1.64 1.50 1.Setup the hypothesis for the test. Choose a or b H0: ρ ▼ A.less than or equals≤ b.less than< c.equals= d.greater than> e.greater than or equals≥ f.not equals≠ 0 Ha: ρ ▼ A. less than or equals≤ b.less than< c.equals= d.greater than> e.greater than or equals≥ f.not equals≠ 0 2.Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) Fill the correct answer A. The critical value is= B. The critical values are −t0= and t0=. Calculate the test statistic. t= (Round to three decimal places as needed.) 3.What is your conclusion? Choose A or B? There ▼ (A)is not (B)is enough evidence at the 1% level of significance to conclude that there ▼ (A)is not (B)is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface.
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.
Weight, x
|
5930
|
5390
|
6500
|
5100
|
5890
|
4800
|
|
---|---|---|---|---|---|---|---|
Variability in braking distance, y
|
1.76
|
1.95
|
1.91
|
1.62
|
1.64
|
1.50
|
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