(a) The table below shows the age of children and the number of words recalled in a memory game. Age Number of Words Recalled 3 5 2 4 5 5 ) Based on the data above, find the linear regression equation to predict the number of words recalled from their age. (1 Calculate the predicted Y value for each of the X value
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
q2 can you show every step on how to do the question do that I can understand the steps clearly. Also attached the regression . please use this formula ya
![(a) The table below shows the age of children and the number of words recalled in a memory game.
Age
Number of Words Recalled
3
5
4
5
6
4
6
(1) Based on the data above, find the linear regression equation to predict the number of words
recalled from their age.
(1) Calculate the predicted Y value for each of the X value](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Feb7c09c8-3b49-4e8b-94ee-a031f37ce4c3%2F34e079ca-0da3-4916-94b6-f9da66082abf%2F5i35dta_processed.jpeg&w=3840&q=75)
![Regression Steps
STEP 1 PEARSON CORRELATION FIND r VALUE
STEP 2 REGRESSION
Regression function
Σx ΣΥ = Σxy -
ỹ = a + bx
Σχ2ΣΥ2 :
N =
ExEy
SP -Σxy -
Slope b regression of line
SP
n
(Ey)?
b
SSx
SSy = Ey2
n
(Σx)2
Y intercept a of regression line
SSy Σχ2
а — Му — ьмх
– bMx
à =
SP
r =
(SSx)(SSy)
STEP 3
F RATIO HYPOTHESIS TESTING REGRESSIION STANDARD ERROR OF ESTIMATE
STEP4
но-
SS resi dual
п — 2
SS regression =r2 SSy
SS residual = (1-r^2 XSSY )
Df regression
Df residual =n – 2
(Numerator)
(denominator)
MS regression = (SS regression)/df regression)
MS residual = (SS residual)/(df residual)
F= (MS regression)/(MS residual)
a 0.05, df = Numerator, Denominator, F Critical (from
the table) =
If F calculated >F Critical , H0 Rejected](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Feb7c09c8-3b49-4e8b-94ee-a031f37ce4c3%2F34e079ca-0da3-4916-94b6-f9da66082abf%2Fydmnej_processed.png&w=3840&q=75)
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