Hi! I'm doing a linear project model for UMUC Math 107. I am using a model someone did of calories in a slice of large cheese pizzato help me with my subject which is the fat and calories in a serving of store bought mac and cheese. I'm having trouble understanding how to get the slope and intercept. I can't figure out how to attach documents so I had to copy and paste the pizza model as well as what I have started. Unfortunately because of that the graphs don't come through. I hope this makes sense. Can Someone help me figure out the next steps?You can see from my model that I am basically using the pizza model as a sample and plugging in my own answers - or trying to at least. This is driving me nuts! Thanks for any help. -Briana

Curve-Fitting Project - Linear Model: Calories in a slice of large cheese pizza

(LR-1)Purpose: To analyze the calories in a slice of large size pizzas using a linear model

Data: The calories were retrieved from http://www.calorieking.com/

DATA:

Calories : one slice of Large

size pizza

Brand

Fat content(g)

Calories

Little ceasers

9

250

Costco food court

28

700

Papa John's pizza

10

290

Pizza hut

13

310

Dominos

11

290

Papa Gino's

11

350

Papa Murphy's

9.5

237

Lou Malnati's

31

590

Cici's Pizza

5

190

(LR-2)SCATTERPLOT:

The trend is somewhat linear.

(LR-3)

Line of Best Fit (Regression Line)

y = 18.389x + 95.829 where x = Fat content (g) and y = calories

(LR-4) The slope is 18.389 and is positive as the calories increases with increase in the fat content.

The slope indicates that in general, calories increase by 18.389 with a unit increase in the fat content.

(LR-5)Values of r² and r:

r² =0.922

We know that the slope of the regression line is positive so the correlation coefficient r must be positive.

Recall that r = -1 corresponds to perfect negative correlation, and so r = .9602 indicates strong positive correlation (close to 1 and is very strong).

(LR-6) Prediction: For fat content of 20 g , substitute x = 20 to get y = y = 18.389(20) + 95.829 = 463.60

The regression line predicts calorie of 463.60 for one slice of large size pizza having fat content of 20 g

(LR-7) Narrative:

The data consisted of calories in a slice of large size cheese pizza of different brands for given fat contents. The data exhibit a strong linear trend, looking overall at the given fat content.

The regression line predicts calorie of 463.60 for one slice of large size pizza having fat content of 20 g.

The correlation coefficient has value .9602 which is quite close to 1 and thus this shows that the calories are very much dependent on fat contents. With increase in the fat content the calories also increases.

Also the data points are quite closer to the regression line which clearly shows that calories are linearly related to the fat content.

CONCLUSION:

Our regression analysis shows that calories and fat contents in a single slice of a large size cheese pizza are very strongly related. With increase in the fat content calorie intake also increases.

Curve-Fitting Project - Linear Model: Calories in a serving of Mac and Cheese

(LR-1)Purpose: To analyze the calories and fat in a serving of store bought macaroni and cheese using a linear model

Data: The calories were retrieved from www.kraft.com, www.mariecallenders.com, www.traderjoes.com, www.bostonmarket.com, www.annies.com, www.verywellfit.com The data is based on a single serving suggestion from each manufacturer

DATA:

Calories : One serving macaroni and cheese

Brand

Fat content(g)

Calories

Kraft Mac & Cheese

3

250

Velveeta

12

360

Stouffer's Homestyle

17

350

Cracker Barrel

10

320

Amy's

16

400

Boston Market

11

310

Joe's Diner

15

360

Marie Callender's

22

570

Annie's

7

270

(LR-2)SCATTERPLOT:

Because of similar ingredients in each serving the trend is somewhat linear.

(LR-3)

Line of Best Fit (Regression Line)

y = 18.389x + 95.829 where x = Fat content (g) and y = calories

(LR-4) The slope is 18.389 and is positive as the calories increases with increase in the fat content.

The slope indicates that in general, calories increase by 18.389 with a unit increase in the fat content.

(LR-5)Values of r² and r:

r² =0.922

We know that the slope of the regression line is positive so the correlation coefficient r must be positive.

Recall that r = -1 corresponds to perfect negative correlation, and so r = .9602 indicates strong positive correlation (close to 1 and is very strong).

(LR-6) Prediction: For fat content of 20 g , substitute x = 20 to get y = y = 18.389(20) + 95.829 = 463.60

The regression line predicts calorie of 463.60 for one slice of large size pizza having fat content of 20 g

(LR-7) Narrative:

The data consisted of calories in a slice of large size cheese pizza of different brands for given fat contents. The data exhibit a strong linear trend, looking overall at the given fat content.

The regression line predicts calorie of 463.60 for one slice of large size pizza having fat content of 20 g.

The correlation coefficient has value .9602 which is quite close to 1 and thus this shows that the calories are very much dependent on fat contents. With increase in the fat content the calories also increases.

Also the data points are quite closer to the regression line which clearly shows that calories are linearly related to the fat content.

CONCLUSION:

Our regression analysis shows that calories and fat contents in a single slice of a large size cheese pizza are very strongly related. With increase in the fat content calorie intake also increases.