A prize is divided in the ratio 5:1 if the larger share is £65 what is the total prize?
2 answers:
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Answer:
We conclude that there is no difference in potential mean sales per market in Region 1 and 2.
Step-by-step explanation:
We are given that a random sample of 12 supermarkets from Region 1 had mean sales of 84 with a standard deviation of 6.6.
A random sample of 17 supermarkets from Region 2 had a mean sales of 78.3 with a standard deviation of 8.5.
Let = mean sales per market in Region 1.
= mean sales per market in Region 2.
So, Null Hypothesis, :
= 0 {means that there is no difference in potential mean sales per market in Region 1 and 2}
Alternate Hypothesis, : >
0 {means that there is a difference in potential mean sales per market in Region 1 and 2}
The test statistics that will be used here is <u>Two-sample t-test statistics</u> because we don't know about population standard deviations;
T.S. = ~
where, = sample mean sales in Region 1 = 84
= sample mean sales in Region 2 = 78.3
= sample standard deviation of sales in Region 1 = 6.6
= sample standard deviation of sales in Region 2 = 8.5
= sample of supermarkets from Region 1 = 12
= sample of supermarkets from Region 2 = 17
Also, =
= 7.782
So, <u><em>the test statistics</em></u> = ~
= 1.943
The value of t-test statistics is 1.943.
Now, at a 0.02 level of significance, the t table gives a critical value of -2.472 and 2.473 at 27 degrees of freedom for the two-tailed test.
Since the value of our test statistics lies within the range of critical values of t, so we have<u><em> insufficient evidence to reject our null hypothesis</em></u> as it will not fall in the rejection region.
Therefore, we conclude that there is no difference in potential mean sales per market in Region 1 and 2.
The slope of the linear regression equation y = 61. 93x - 1. 79 is attached with the response.
<h3>What is linear regression?</h3>Linear regression is the most primary and generally used predictive research. Regression calculations are utilised to represent data and to describe the association.
The slope of the linear regression equation represented in the graph is the straight-line cutting the intercepts on the y axis at 1.79.
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