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Condition #1
Random Sample Stated
Condition #2
n<10% of all [total population]
Condition #3
Scatterplot shows a clear linear trend
Condition #4
Residual plot shows random and equal scatter
Condition #5
Dot plot of residuals doesn’t show any major skewness or outliers
Test Statistic
t=b/SEb
df
n-2
Confidence Interval
b±t*SEb
Ho: B=0
There is no true linear relationship between x and y
Ha: B≠0
There is a linear relationship between x and y
Ha: B<0
There is a negative linear association between x and y
Ha: B>0
There is a positive linear association between x and y
Step 1 Parameter
Where B is the true slope relating [y in context] and [x in context]
Step 2
One Sample t-Test for Slope
Step 3
If Ho: B=0 is true,
P(b[sign]0)=P(t[sign][value])
Step 4 (Reject Ho)
There is evidence of a linear relationship between [y in context] and [x in context].
Step 4 (Fail to Reject Ho)
There is not evidence of a linear relationship between [y in context] and [x in context].
Calculation
Formula, Numbers in Formula, EST MOE, Interval, df
Confidence Interval Interpretation
We are [%] confident that the true slope relating [y in context] and [x in context] is between [value] and [value].
a (Bo) (y-intercept)
When [x in context] is 0, we predict [y in context] as a
b (B1) (slope)
For each 1 increase in [x in context], the predicted [y in context] increases/decreases by b
σ (s) (standard deviation)
The actual [y in context] typically varies by about s from the predicted [y in context]
Standard Error of the Slope Interpretation (SEb)
If we repeated the random sampling many times, the slope of the sample regression line would typically vary by about SEb from the slope of the true regression line for predicting the [y in context] from the [x in context]
Weak r
0-0.55
Moderately weak r
0.55-0.70
Moderately strong r
0.70-0.85
Strong r
0.85-1.0
r² interpretation
Approximately % of the variation in the predicted [y in context] is explained by the LSRL with [x in context]