A study is conducted to determine if there is a relationship between the two variables, blood haemoglobin (Hb) levels and packed cell volumes (PCV) in the female population. A simple linear regression analysis was performed using SPSS. Based on the SPSS output of the ANOVA table, which of the following statements is the CORRECT interpretation? 1. The regression model statistically significantly predicts the blood haemoglobin level. 2. About 39.98 % of variance in Hb is explained by PCV. 3. The regression model does not fit the data. 4. There is significant contribution of Hb towards PCV.

A study is conducted to determine if there is a relationship between the two variables, blood haemoglobin (Hb) levels and packed cell volumes (PCV) in the female population. A simple linear regression analysis was performed using SPSS. Based on the SPSS output of the ANOVA table, which of the following statements is the CORRECT interpretation? 1. The regression model statistically significantly predicts the blood haemoglobin level. 2. About 39.98 % of variance in Hb is explained by PCV. 3. The regression model does not fit the data. 4. There is significant contribution of Hb towards PCV.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.5: Comparing Sets Of Data

Problem 12PPS

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Question

1. The regression model statistically significantly predicts the blood haemoglobin level.

2. About 39.98 % of variance in Hb is explained by PCV.

3. The regression model does not fit the data.

4. There is significant contribution of Hb towards PCV.

ANOVA
Sum of
Model
Squares
df
Mean Square
F
Sig.
1
Regression
24.250
1
24.250
39.982
.000
Residual
7.278
12
.607
Total
31.529
13
a. Dependent Variable: Hb
b. Predictors: (Constant), PCV

Definition Definition Statistical method that estimates the relationship between a dependent variable and one or more independent variables. In regression analysis, dependent variables are called outcome variables and independent variables are called predictors.

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