Multiple regression Essay

This essay has a total of 868 words and 5 pages.

multiple regression



Multiple regressions and multiple correlations deals with the relationship of one variable
compared with a number of other variables. It is similar to the bivariate correlation
because it describes the degree of linear relationship between two variables. Though the
multiple correlation assigns one variable to be the criterion or dependent variable and
the other variable is the total of the independent variable or predictor. The total of the
independent variable is found by computing weights so the correlation between the
predictor and the criterion is as large as possible. The multiple regression is better
than the bivariate regression because it examines curvilinear relationships between
variables and investigates interactions between continuous independent variables.

In the present experiment the multiple regression and multiple correlation was used to
compare different predictors of reading skills. It was thought that working memory ability
was not as good of a predictor of reading skill as exposure to print. The results from
this experiment showed that the overall correlation was significant, (R = .59, F (5,147)
= 15.45, p * .0001) meaning adding one adding one variable to the prediction equation
significantly increases the degree of multiple correlation. It was also shown that the
best predictors for reading ability were exposure to print (t (142) = 5.501, p * .0001)
and non-verbal ability measured by the Ravenís test (t (142) = 2.953, p * .01). The
standardized regression equation for the significant coefficients was Z reading = .385Z
ART .224Z Ravens. The partial correlation, meaning the correlation between two variables
after variations from other variables were removed was .419 for exposure to print and .219
for non-verbal ability. The standardized regression equation for all the predictors was
Z reading= .119Z workmem .385Z ART .224Z Ravens .080Z GPA .150Z IQ.

Using a stepwise regression analysis it was found that the overall regression was
significant, (R = .574, F (5,147) = 23.623, p * .0001). The best predictors for reading
ability were exposure to print (t (142) = 5.884, p * .0001) non-verbal ability (t (142) =
3.279, p * .001) and IQ (t (142) = 2.181, p * .05) and the predictors had partial
correlations of, .440, .264 and .179, respectively. The regression equation in
standardized form for the stepwise regression analysis was Z reading = .408Z ART .248Z
Ravens .168ZIQ. The difference between the first standardized regression equation was
the stepwise regression only includes the variables that significantly contributed to the
prediction. Therefore, working memory and GPA were excluded in the stepwise regression
because they were not good predictors of reading ability. It was also shown that the
stepwise regression was a better type of regression because it was able to significantly
predict predictors (F (2, 140) = 2.278, ns).

For the curve estimates the regression coefficient was significant for the linear
component (t (1,146) = 6.150, p * .00001) therefore there was a positive linear
relationship between reading ability and exposure to print. The regression coefficients
were not significant for the quadratic component (t (1,146) = -.583, ns) or the cubic
Continues for 3 more pages >>




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