StatCrunch logo (home)

Report Properties

from Flickr
Created: Nov 12, 2017
Share: yes
Views: 517
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.
effects of smoke on infants part 2
Mail   Print   Twitter   Facebook


Data set 1. effect of smoke on infants   [Info]
To analyze this data, please sign in.



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

This scatterplot is graphing the quantitative variables of weight and length.

No outliers should be removed. 

My p-value should be very close to 0.

My line of best fit will have a slope of 0.0024 and a standard deviation of 3.5. 


Result 2: Simple Linear Regression   [Info]
Simple linear regression results:
Dependent Variable: Length in cm
Independent Variable: Weight in Grams
Length in cm = 42.401967 + 0.0023777771 Weight in Grams
Sample size: 57
R (correlation coefficient) = 0.48952036
R-sq = 0.23963018
Estimate of error standard deviation: 3.5415865

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept42.4019671.9467454 ≠ 05521.780951<0.0001
Slope0.00237777710.00057112538 ≠ 0554.16331880.0001

Analysis of variance table for regression model:

My correlation coefficient is 0.48952036, therefore, there is a moderate positive relationship.

Yes, my terms are significant. 

My line of best fit has an intercept of 42.4 and a slope of 0.00237.

My R-sq is 0.2396 which expressed how well the regression line approximates the data points. 


Result 3: Simple Linear Regression line of best fit   [Info]
Right click to copy

No, it is not a good fit for my data because an R-sq of 1 would mean that the line perfectly represents the data and my R-sq is 0.23 which isn't even close to being perfect.

No, my data is not correlated.


Result 4: Simple Linear Regression QQ residuals   [Info]
Right click to copy

Yes, my expected values follow a normal distribution and appear to be left-skewed. 


Result 5: Simple Linear Regression predicted values vs   [Info]
Right click to copy

Yes, my residual plot implies that the linear model is a good fit.

No, the results were the same.

Yes, it is different than the model for my whole data set because there was less data that was more alike and therefore made the line of best fit almost perfect.

Result 6: Multiple Linear Regression 2   [Info]
Multiple linear regression results:
Dependent Variable: Weight in Grams
Independent Variable(s): Gestation Period (Weeks), Length in cm
Weight in Grams = -7532.7019 + 194.34399 Gestation Period (Weeks) + 68.951014 Length in cm

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-7532.70191288.8147 ≠ 054-5.8446739<0.0001
Gestation Period (Weeks)194.3439930.159715 ≠ 0546.4438272<0.0001
Length in cm68.95101419.020375 ≠ 0543.62511330.0006

Analysis of variance table for multiple regression model:

Summary of fit:
Root MSE: 553.25463
R-squared: 0.5702
R-squared (adjusted): 0.5542

This data now represents all my quantitative data. It includes the gestation period which hasn't been included in this part of the project. This changed my R-sq.

HTML link:
<A href="">effects of smoke on infants part 2 </A>

Want to comment? Subscribe
Already a member? Sign in.
By xg15
Nov 18, 2017

The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. Usually it is 0.05 or 0.01.
When determine whether a slope is significant or not, we compare the P-value with the significance level
R^2 is the percentage of variance of response explained by linear model

Always Learning