QUICK QUESTION: did I do this right?

A student working on a summer internship in the economic research department of a large corporation studied the relation between

OverLord2011 [107]

Answer:

Step-by-step explanation:

Hello!

Y: sales of a product in a marketing district (million dollars)

X: population in a marketing district (million persons)

The objective is to test if there is a linear association between the sales of a product and the population of a marketing district.

Parameter; Estimated Value; 95 Percent Confidence limits

Intercept 7.43119 -1.18518 16.0476

Slope 0.755048 0.452886 1.05721

a.

To test if there is or not an association between these two variables, you have to do a hypothesis test for the slope. If the slope is equal to zero, there is no linear association between the two variables, if the slope is different from zero, then there is a linear association between the variables.

So the student's hypotheses are:

H₀: β = 0

H₁: β ≠ 0

The data the student used to conclude is the 95%CI for the slope [0.452886;1.05721]

To be able to decide over an hypothesis test using a confidence interval there are several conditions to be met, one of them is that the confidence level and the significance level should be complementary, this means that if the interval was constructed using 1 - α= 0.95, then the hypothesis test should be conducted with a significance level of α= 0.05.

Considering that the value of the slope stated in the null hypothesis is not included in the given interval, i.e. zero is not included in the CI, then the decision is to reject the null hypothesis.

Then it can be concluded that there is a linear association between the sales of a product and the population in a marketing district.

b.

Although in the context of the variables of study it makes no sense that the estimate of the intercept takes negative numbers, keep in mind that mathematically if possible and correct. This means that obtaining a negative estimate of the intercept does not represent a calculation error or problem for the regression model. In general, when this occurs, a footnote is made indicating has no biological meaning or sense in context.

After all, what you have done with the estimate is a mathematical assertion of the social variables.

I hope it helps!

3 0

3 years ago

I'll mark brainly pls do this

lidiya [134]

Step-by-step explanation:

Verbs are read, complete, checked, work, invite. Pronouns are their, your, and them. Nouns are Classroom, Zeroes, Friday, February, friends, assignment, students, trimester. Adjectives are simple, missing.

Hope this helps! If I missed some or got some wrong, please tell me! Goodbye!

8 0

3 years ago

Read 2 more answers

PLZZZZ HELP FAST!!!!

Volgvan

Divide by 6... For part E, you should know every ingredient for 1 serving by then, so mu multiply by 10

4 0

3 years ago

Read 2 more answers

How would you do these questions?

Delvig [45]

Part 1

Population = {1,2,4}

Sample 1 = {1,1}, probablity = 1/9

Sample 2 = {1,2}, probablity = 1/9

Sample 3 = {1,4}, probablity = 1/9

Sample 4 = {2,1}, probablity = 1/9

Sample 5 = {2,2}, probablity = 1/9

Sample 6 = {2,4}, probablity = 1/9

Sample 7 = {4,1}, probablity = 1/9

Sample 8 = {4,2}, probablity = 1/9

Sample 9 = {4,4}, probablity = 1/9

Each of the 9 samples has equal probablity of occuring, so that's why each probablity is 1/9

Note: it may be best to write the above in the form of a table

==================================================

Part 2

For each sample (1 through 9), compute the mean. So we'll add up the values and divide by 2

xbar represents the sample mean

Sample 1 = {1,1}, xbar = 1

Sample 2 = {1,2}, xbar = 1.5

Sample 3 = {1,4}, xbar = 2.5

Sample 4 = {2,1}, xbar = 1.5

Sample 5 = {2,2}, xbar = 2

Sample 6 = {2,4}, xbar = 3

Sample 7 = {4,1}, xbar = 2.5

Sample 8 = {4,2}, xbar = 3

Sample 9 = {4,4}, xbar = 4

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Now let's list out each possible xbar value and its associated probability

xbar = 1, probability = 1/9

xbar = 1.5, probability = 2/9

xbar = 2, probability = 1/9

xbar = 2.5, probability = 2/9

xbar = 3, probability = 2/9

xbar = 4, probability = 1/9

note how the probabilities listed above add to 9/9 = 1.

Again a table is ideal to help organize the data.

==================================================

Part 3

To calculate the value of mu_x, we add up all the xbar values found in part 2, and divide by 9

(1+1.5+2.5+1.5+2+3+2.5+3+4)/9 = 2.333

So mu_x = 2.333 approximately

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Calculating the standard deviation is a bit lengthier, but you subtract each data value from the mean, square each difference, and add up the squares.

Afterward, you divide by n = 9 to get the population variance. Apply the square root to get the population standard deviation.

Luckily a calculator or spreadsheet can make quick work of this. You should get roughly 0.88192 as the standard deviation.

==================================================

Part 4

The mean of the population {1,2,4} is (1+2+4)/3 = 2.3333 which matches with mu_xbar

The standard deviation of the population {1,2,4} is roughly 0.88191710368819 which matches with 0.88192 found earlier back in part 3.

6 0

3 years ago

What is half of a teaspoon of vanilla

Ksivusya [100]

Answer:

I believe the answer would be: Half of a teaspoon of vanilla.

Step-by-step explanation:

6 0

3 years ago