StatCrunch logo (home)

Report Properties

from Flickr
Owner: gmm16d
Created: Nov 12, 2017
Share: yes
Views: 702
Results in this report
Data sets in this report
Need help?
To copy selected text, right click to Copy or choose the Copy option under your browser's Edit menu. Text copied in this manner can be pasted directly into most documents with formatting maintained.
To copy selected graphs, right click on the graph to Copy. When pasting into a document, make sure to paste the graph content rather than a link to the graph. For example, to paste in MS Word choose Edit > Paste Special, and select the Device Independent Bitmap option.
You can now also Mail results and reports. The email may contain a simple link to the StatCrunch site or the complete output with data and graphics attached. In addition to being a great way to deliver output to someone else, this is also a great way to save your own hard copy. To try it out, simply click on the Mail link.
Nutritional Data for Fast Food 2017 Report Part 2
Mail   Print   Twitter   Facebook


Data set 1. Nutritional Data for Fast Food 2017   [Info]
To analyze this data, please sign in.

Result 1: Result 1: Scatter Plot   [Info]
Right click to copy

In my data, there aren’t any outliers as all of the data follows the same linear pattern. The data shows a strong positive correlation. Since the data is strongly correlated, an appropriate significance level for the data would be .01 as the correlation is almost perfect and the P-levels for both calories and total fat are less than .0001. Since the slope is .0684 and the y-intercept is -7.8836, the line of best fit is y=.0684x-7.8836.

Result 2: Result 2: Simple Linear Regression   [Info]
Simple linear regression results:
Dependent Variable: Total Fat (g)
Independent Variable: Calories
Total Fat (g) = -7.8836043 + 0.068410501 Calories
Sample size: 126
R (correlation coefficient) = 0.94076085
R-sq = 0.88503097
Estimate of error standard deviation: 6.2098677

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-7.88360431.3023962 ≠ 0124-6.0531534<0.0001
Slope0.0684105010.002214232 ≠ 012430.895815<0.0001

Analysis of variance table for regression model:

The R value, also known as the correlation coefficient, is .94. Since this value in very close to 1, it validates the notion that the data shows a strong positive correlation. The P-values for both the intercept and slope are less than .0001, indicating strong evidence against the null hypothesis, or in other words, a .01% chance that the null hypothesis is true. These extremely small P-values also indicate that the terms are statistically significant and the results are not simply a product of random sampling. The line of best fit extends from the bottom left hand corner of the graph to the upper right hand corner of the graph, demonstrating a positive correlation. The line accurately represents the data and its strong correlation as it runs through the area in which the data points are concentrated in. R^2 is .88, and since it is close to 1, this indicates that the line of regression, also known as the line of best fit, accurately fits the data.


Result 3: Result 3: Scatter Plot   [Info]
Right click to copy

The data did not contain outliers. The data is correlated as evidenced by its strong positive trend and correlation. Despite this strong correlation, causation cannot be assumed. For example, although grams of total fat increases as the amount of calories increases, other nutritional factors that accompany a heavy amount of calories may serve as an underlying cause prompting the total fat to increase linearly.


Result 4: Result 4: QQ plot of residuals   [Info]
Right click to copy

The expected values follow a normal distribution as both sets evidently derive from a population within the same distribution, shown by the data falling directly on the reference line.

Result 5: Result 5: Residuals Linear Regression   [Info]
Right click to copy

The residual plot implies that the linear model is a good fit because it is symmetrically distributed and most of the data points are conglomerated in the middle of the graph while the points do not exhibit an obvious pattern.

Result 6: E.C #3- .99 significance level   [Info]
Right click to copy

After changing the significance level to .99, I did not get different results.

Result 7: E.C #4- linear model for cluster sample   [Info]
Right click to copy

The linear model for the cluster sample, which contains a sample size of 50 per variable (calories and total fat), is much different than it is for the whole data set. While the entire data set maintains a strong positive correlation, the sample maintains a random distribution with no correlation.

Result 8: E.C. 5-Multiple Linear Regression   [Info]
Multiple linear regression results:
Dependent Variable: Total Fat (g)
Independent Variable(s): Calories, Serving Size (g), Saturated Fat (g), Trans Fat (g), Sodium (mg), Carbs (g), Sugars (g), Protein (g)
Total Fat (g) = -0.2483677 + 0.091703224 Calories + 0.0039963241 Serving Size (g) + 0.47823524 Saturated Fat (g) + -2.0581851 Trans Fat (g) + -0.00074598736 Sodium (mg) + -0.32706204 Carbs (g) + -0.08810608 Sugars (g) + -0.30616988 Protein (g)

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-0.24836770.78902322 ≠ 0105-0.314778690.7536
Calories0.0917032240.0048149957 ≠ 010519.045339<0.0001
Serving Size (g)0.00399632410.0077869115 ≠ 01050.513210410.6089
Saturated Fat (g)0.478235240.10380166 ≠ 01054.6072021<0.0001
Trans Fat (g)-2.05818510.56140596 ≠ 0105-3.66612620.0004
Sodium (mg)-0.000745987360.0011984835 ≠ 0105-0.622442760.535
Carbs (g)-0.327062040.03516329 ≠ 0105-9.3012357<0.0001
Sugars (g)-0.088106080.029200908 ≠ 0105-3.01723770.0032
Protein (g)-0.306169880.051856274 ≠ 0105-5.9042014<0.0001

Analysis of variance table for multiple regression model:

Summary of fit:
Root MSE: 2.5309629
R-squared: 0.9809
R-squared (adjusted): 0.9795

The P-values for calories, saturated fat, carbs, and protein are are less than .0001, indicating strong evidence against the null hypothesis and that the results were not obtained by a random sample. Trans Fat and sugars also maintain a very low P-value, indicating the same properties. In contrast, the P-values for the intercept, serving size, and sodium are all larger than .05, demonstrating weak evidence against the null hypothesis. R^2 is very close to one, indicating that the line of regression, also known as the line of best fit, accurately fits the data.

HTML link:
<A href="">Nutritional Data for Fast Food 2017 Report Part 2</A>

Want to comment? Subscribe
Already a member? Sign in.
By xg15
Nov 15, 2017

Excellent report. One point about the cluster sample regression: the cluster should be selected in a clustered way, like calories and fat in McDonald's or Burger King, etc.

Always Learning