Question

Medical professionals can find relationships between variables. The more you drink alcohol, the less functionality of your liver. The less carbohydrates a person intakes, the lower their Body Mass Index. Data can be collected and organized as an ordered pair (x, y). The data can be analyzed to determine the type and strength of a correlation and to calculate a regression line in order to make a prediction.

Use the internet to find a data set of ordered pairs. Key terms to search: Free Public Data Sets and Medical Data Sets. Something that is current and in the United States of America, please ? Create a Post: Introduce your Data Set and Cite the Source. Which would be the independent variable, and which would be the dependent variable? Without drawing a scatter plot, would you expect a positive, negative or no correlation? Explain.

Would you categorize your data to have a strong or weak correlation? Why? What would the r2 value tell you about the data that you selected? What is the equation of the regression line? Use the regression line to make a prediction about the data you collected.

Nothing to do with women bone density please and please leave website where information was found as reference :)

Answer 1

Solution:-

Hi,

The sample data I discovered online shows the relationship between ice cream sales for particular days and the noon-temperature of that day (Scatter Plots, n.d.). The data set is shown below.

Temperature	Ice Cream Sales ($)
14.2	215
16.4	325
11.9	185
15.2	332
18.5	406
22.1	522
19.4	412
25.1	614
23.4	544
18.1	421
22.6	445
17.2	408

1. Which would be the independent variable, and which would be the dependent variable?

The independent variable would be the temperature and the dependent variable would be the ice cream sales

2. Without drawing a scatter plot, would you expect a positive, negative or no correlation? Explain.

It can be observed from the table that as the temperature increases, then the ice cream sales would increase. Therefore, a positive correlation between the temperature and ice cream sales is expected when drawing the scatter plot

3. Would you categorize your data to have a strong or weak correlation? Why?

There seems to a strong correlation as data seems to fall close to line more or less.

4. What would the value tell you about the data that you selected?

Using Excel

data -> data analysis -> Regression

= 0.9168

It means 91.68 % of variation in Ice cream sales can be explained by Temperature

5. What is the equation of the regression line?

Sales^ = -159.4742 + 30.0879 Temp

6. Use the regression line to make a prediction about the data you collected.

For Temp = 25°C

Sales^ = -159.4742 + 30.0879*25

= 592.72

Therefore, the predicted ice cream sales would be $592.72 when the temperature during that day is 25°C

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