Question

**Please answer the question briefly and
clearly.**

1. The best model to describe a linear relationship between two variables is the _______.

2. An assumption of linear regression states that for each value
of *X*, there is a group of *Y* values that are
statistically (*Independent*) and (*normally
distributed*) about the regression line.

3. If the slope of a regression line is zero, the orientation of
the regression line is (*Flat, or Constant)*, which
indicates a lack of a relationship.

4. For an inverse relationship between two variables, the sign of the correlation coefficient is __________.

5. The standard error of the estimate measures the scatter or
dispersion of the observed values around a (*Mean of Observed
value which is a predicted value* ).

Answer #1

In question no .2,3 and 5 it is not mentioned clearly that what is being asked. Hence, i am answering question nos. 1 and 4.

Answer 1. For describing linear relationship you can assume model

Y= a + bX

This type of relation may be described by a straight line. The intercept that the line makes on the Y-axis is given by 'a' and the slope of the line is given by 'b'. It gives the change in the value of Y for very small change in the value of X. For estimating a and b, you can make the use of least squares principle.

Answer 2. For an inverse relationship between two variables, the correlation coefficient r<0, hence its its sign has to be negative. This type of relation is shown on a scatter diagram as having a downward sloping line and the variables move in opposite directions.

The proportion of explained variation is called the
__________________________________________
An assumption of linear regression states that for each value of
X, there is a group of Y values that are
statistically __________________ and normally distributed about the
regression line.
________________________________________
.
For an inverse relationship between two variables, the sign of
the correlation coefficient is __________________.
The standard error of the estimate measures the scatter or
dispersion of the observed values around a
__________________________________________________________

A scatter diagram is a graph that portrays a correlation between
a ________________variable and a _______________ variable.
The _________________ of _________________ is expressed as a
percent, its value is between 0 and 100%.
In plotting paired data in a scatter diagram, the independent
variable is scaled on the __________________.
If there is absolutely no relationship between two variables,
Pearson's r will equal _____.
________________________________________
If the coefficient of correlation is 0.80, the coefficient of
determination is _____.
________________________________________
The proportion...

21) A study was recently conducted by Major League Baseball to
determine whether there is a correlation between attendance at
games and the record of home team's opponent. In this study, the
dependent variable would be _________________.
22) A correlation of -0.9 indicates a __________ relationship
between the variables.
23) The values of the regression coefficients are found such the
sum of the residuals is ___________.
24) If the R-square value for a simple linear regression model
is 0.80 and...

Which of the following statements concerning the linear
correlation coefficient are true?
I: If the linear correlation coefficient for two variables is
zero, then there is no relationship between the variables.
II: If the slope of the regression line is negative, then the
linear correlation coefficient less than one.
III: The value of the linear correlation coefficient always lies
between -1 and 1.
IV: A linear correlation coefficient of 0.62 suggests a stronger
linear relationship than a linear correlation coefficient...

1) Regression:
a) is a statistical technique that is not influenced by
outliers.
b) identifies the resistance of a relationship.
c) measures the strength but not the direction of a
relationship.
d) can be used for prediction.
2) The best-fitting regression line:
a) has the smallest total standard error.
b) has the smallest slope.
c) has the largest total squared error of prediction.
d) is the line with the smallest (Y – Ŷ)2 value.
If the slope (b) of the...

Which of the following statements concerning regression and
correlation analysis is/are true?
A. If the correlation coefficient is zero, then there is no linear
relationship between the two variables.
B. A negative value for the correlation coefficient indicates that
high values of the independent variable are correlated with low
values of the dependent variable.
C. The slope coefficient for a simple linear regression model
measures the expected change in the independent variable for a unit
change in the dependent variable....

Please choose the most appropriate description of the concepts
related to the linear model.
The linear relation between two continuous variables
Pearson's r asks the opposite question of this
Type of plot that depicts the relation between X and Y
Person's r has no error term in its formula
Pearson's r can compare two sets of numbers when n1 does not
equal n2
Prediction using the linear model
The point on Y when X=0
The rate of change as one...

Which of the following statements concerning the linear
correlation coefficient is/are true?
A: If the linear correlation coefficient for two variables is zero,
then there is no relationship between the variables.
B: If the slope of the regression equation is negative, then the
linear correlation coefficient is negative.
C: The value of the linear correlation coefficient always lies
between -1 and 1 inclusive.
D: A correlation coefficient of 0.62 suggests a stronger linear
relationship than a correlation coefficient of -0.82.

Match the statistics term with its BEST
definition.
Question 2 options:
A key requirement for using correlation and regression models is
to collect this type of data.
With bivariate data, the result of MINIMIZING the sum of squared
distances between the observed and predicted values (residuals) for
a linear model.
This quantity is computed by subtracting the observed response
variable from the predicted response variable.
With bivariate data, when one variable increases a second
variable decrease implies this relationship.
A...

According to the General Linear Model (GLM), the "best fitting"
mathematical explanation of the relationship between the variables
is called:
a. Equivalence test
b. Regression line
c. Significace
d. Variance line

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