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WorksheetsStatistics
Total questions: 143
Worksheet time: 1hrs 12mins
A process by which we estimate the value of dependent variable on the basis of one or more independent variables is called:
Correlation
Regression
Residual
Slope
The method of least squares dictates that we choose a regression line where the sum of the square of deviations of the points from the lie is:
Maximum
Minimum
Zero
Positive
A relationship where the flow of the data points is best represented by a curve is called:
Linear relationship
Nonlinear relationship
Linear positive
Linear negative
All data points falling along a straight line is called:
Linear relationship
Non linear relationship
Residual
Scatter diagram
The value we would predict for the dependent variable when the independent variables are all equal to zero is called:
Slope
Sum of residual
Intercept
Difficult to tell
The predicted rate of response of the dependent variable to changes in the independent variable is called:
Slope
Intercept
Error
Regression equation
The slope of the regression line of Y on X is also called the:
Correlation coefficient of X on Y
Correlation coefficient of Y on X
Regression coefficient of X on Y
Regression coefficient of Y on X
In simple linear regression, the numbers of unknown constants are:
One
Two
Three
Four
In simple regression equation, the numbers of variables involved are:
0
1
2
3
If the value of any regression coefficient is zero, then two variables are:
Qualitative
Correlation
Dependent
Independent
The straight line graph of the linear equation Y = a + bX, slope will be downward If:
b>0
b<0
b=0
b≠0
The straight line graph of the linear equation Y = a + bX, slope is horizontal if:
b=0
b≠0
b=1
a=b
If regression line of Y= 5, then value of regression coefficient of Y on X is:
0
0.5
1
5
If Y = 2 - 0.2X, then the value of Y intercept is equal to:
-0.2
2
0.2X
-2
The dependent variable is also called except one:
Regressand variable
Predictand variable
Explained variable
Independent variable
In the regression equation Y = a+bX, the Y is called:
Independent variable
Dependent variable
Continuous variable
Estimate
In the regression equation Y = a +bX, a is called:
X-intercept
Y-intercept
Dependent variable
Independent variable
The regression equation always passes through:
(X, Y)
(a, b)
mean X, Mean Y
Mean X
The graph showing the paired points of (Xi, Yi) is called:
Scatter diagram
Histogram
Historigram
Pie diagram
When regression line passes through the origin, then:
Intercept is zero
Regression coefficient is zero
Correlation is zero
Association is zero
The purpose of simple linear regression analysis is to:
Predict one variable from another variable
Replace points on a scatter diagram by a straight-line
Measure the degree to which two variables are linearly associated
Obtain the expected value of the independent variable
The sum of the difference between the actual values of Y and its values obtained from the fitted regression line is always:
Zero
Positive
Negative
Minimum
If all the actual and estimated values of Y are same on the regression line, the sum of squares of error will be:
Zero
Minimum
Maximum
Unknown
A measure of the strength of the linear relationship that exists between two variables is called:
Slope
Intercept
Correlation coefficient
Regression equation
When the ratio of variations in the related variables is constant, it is called:
Linear correlation
Nonlinear correlation
Positive correlation
Negative correlation
If both variables X and Y increase or decrease simultaneously, then the coefficient of correlation will be:
Positive
Negative
Zero
One
If the points on the scatter diagram indicate that as one variable increases the other variable tends to decrease the value of r will be:
Perfect positive
Perfect negative
Negative
Z
If the points on the scatter diagram show no tendency either to increase together or decrease together the value of r will be close to:
-1
+1
0.5
0
If one item is fixed and unchangeable and the other item varies, the correlation coefficient will be:
Positive
Negative
Zero
Undecided
In scatter diagram, if most of the points lie in the first and third quadrants, then coefficient of correlation is:
Negative
Positive
Zero
Constant
the two series move in reverse directions and the variations in their values are always proportionate, it is said to be:
Negative correlation
Positive correlation
Perfect negative correlation
Perfect positive correlation
The value of the coefficient of correlation r lies between:
0 and 1
-1 and 0
-1 and +1
-0.5 and +0.5
if the correlation coefficient r = 0.5 then the coefficient of determination is
0.10
. 0.25
1.00
2.50
The range of regression coefficient is:
-1 to +1
0 to 1
-∞ to +∞
0 to ∞
The signs of regression coefficients and correlation coefficient are always:
Different
Same
Positive
Negative
The arithmetic mean of the two regression coefficients is greater than or equal to:
-1
+1
0
r
In simple linear regression model Y = α + βX + ε where α and β are called:
Estimates
Parameters
Random errors
Variables
Negative regression coefficient indicates that the movement of the variables are in:
Same direction
Opposite direction
Same and opposite direction
Difficult to tell
Positive regression coefficient indicates that the movement of the variables are in:
Same direction
Opposite direction
Upward direction
Downward direction
If the value of regression coefficient is zero, then the two variable are called:
Independent
Dependent
Independent and dependent
Difficult to tell
The term regression was used by:
Newton
Pearson
Spearman
Galton
In the regression equation Y = a + bX, b is called:
Slope
Regression coefficient
Intercept
Slope and Regression coefficient
When the two regression lines are parallel to each other, then their slopes are:
Zero
Different
Same
Positive
In the regression equation Y = a + bX, where a and b are called:
Constants
Estimates
Parameters
Intercept and slope
A perfect positive correlation is signified by:
0
-1
+1
-1 to +1
A perfect negative correlation is signified by:
0
-1
+1
-1 to +1
In regression analysis, the variable that is being predicted is
the independent variable
the dependent variable
usually denoted by x
slope
In the regression equation y = bo + bx, bo is the
slope of the line
independent variable
y intercept
parameter
In the regression equation y = bo + b1x1, b1 is the
slope of the line
independent variable
y intercept
parameter
In regression analysis, the variable that is doing the predicting or explaining is
the independent variable
usually denoted by y
the dependent variable
the slope
The coefficient of determination (r2) is
the square root of the correlation coefficient
usually less than zero
the correlation coefficient squared
100%
The value of the coefficient of determination (r2) ranges between
-1 to +1
-1 to 0
1 to infinity
0 to +1
The coefficient of correlation
is the coefficient of determination squared
is the square root of the coefficient of determination
can never be negative
can never be positive
If the slope of the regression equation y = bo + b1x is positive, then
as x increases y decreases
as x increases y increases
as x decrease y decreases
as x decrease y increases
The strength (degree) of the correlation between a set of independent variables X and a dependent variable Y is measured by
Coefficient of Correlation
Coefficient of Determination
Standard error of estimate
Sample size
The percent of total variation of the dependent variable Y explained by the set of independent variables X is measured by
Coefficient of Correlation
Coefficient of Skewness
Coefficient of Determination
Standard Error or Estimate
A coefficient of correlation is computed to be -0.95 means that
The relationship between two variables is weak.
The relationship between two variables is strong and positive
The relationship between two variables is strong and but negative
Correlation coefficient cannot have this value
Let the coefficient of determination computed to be 0.39 in a problem involving one independent variable and one dependent variable. This result means that
The relationship between two variables is negative
The correlation coefficient is 0.39 also
39% of the total variation is explained by the independent variable
39% of the total variation is explained by the dependent variable
Relationship between correlation coefficient and coefficient of determination is that
both are unrelated
The coefficient of determination is the coefficient of correlation squared
The coefficient of determination is the square root of the coefficient of correlation
both are equal
The coefficient of determination is the
ratio of the explained variation to the total deviation.
ratio of the unexplained deviation to the explained deviation.
ratio of the unexplained deviation to the total variation.
ratio of the explained variation to the total variation.
In correlation both variables are always
Random
Non Random
Same
Different
If all the values fall on the same straight line and the line has a positive slope then what will be the value of the correlation coefficient ‘r’:
0≤r≤1
r≥0
r = +1
r = -1
The best fitting trend is one for which the sum of squares of error is
Zero
Minimum (Least)
Maximum
Less than 1
When there is no linear correlation between two variables, what will the value of r be?
-1
+1
0
Negative number
is the slope of the line y = -3.4x - 2.5
-2.5
2.5
-3.4
3.4
An r value of 0.80 indicates:
No linear correlation
Perfect linear correlation
Correlation but not linear
Strong linear correlation
The measure of how well the regression line fits the data is the:
coefficient of determination
mean square error
slope of the regression
standard error
The correlation coefficient, r, can take on any value within what range?
r≥1
0≤r≤1
-1 ≤ r
-1 ≤ r ≤ 1
My estimated regression line is Y = 17 + 4X. The intercept is equal to:
17
4
21
13
If two variables have a correlation coefficient of .30, what percentage of one variable is accounted for by the other variable?
30%
70%
10%
9%
If you have 20 pairs of subjects what would be the degrees of freedom for a test of correlation between the groups of scores?
22
21
20
19
A scatterplot is a
one-dimensional graph of randomly scattered data.
two-dimensional graph of a straight line.
two-dimensional graph of a curved line.
two-dimensional graph of data values.
Two variables have a positive association when
the values of one variable tend to increase as the values of the other variable increase.
the values of one variable tend to decrease as the values of the other variable increase.
the values of one variable tend to increase irregard less of how the values of the other
variable change.
the values of both variables are always positive.
Correlation and regression are concerned with
the relationship between two categorical variables.
the relationship between two quantitative variables.
the relationship between a quantitative explanatory variable and a categorical response
variable.
the relationship between a categorical explanatory variable and a quantitative response
variable.
If the Pearson Correlation Coefficient shows zero value, it means that:
There is no relationship between the two variables
There is a relationship between the two variables
There is a weak relationship between the two variables
There is a strong relationship between the two variables
The most commonly used formula to describe linear relationship is
ŷ = b0 + b1x + b2x2
ŷ = b0 + b1x2
ŷ = b0 + b1x
ŷ = b0 + b1x+b2
A statistical technique that develops an equation that relates a dependent variable to one or more independent variables is called:
Correlation analysis
Regression analysis
Partial correlation analysis
Inference
The total variation explained by a regression model is given by:
R2
The t-value
The f-value
The p-value
The degree of linear association between two metric scaled variables is measured by:
Pearson correlation coefficient
significance level
analysis of variance
β
R2 is used in regression analysis to:
Measure model fit
Measure the amount of variance in the dependent variable explained by variation in the independent variables
To determine how well the model works
Estimate correlation
Correlation analysis is used to:
Simultaneously compare the effect of multiple independent variables on a dependent
variable
Predict values of y based on values of x
Measure the strength of association between two variables
Analyse data
The difference between regression analysis and correlation analysis is (except one):
Regression enables prediction of the dependent variable
Regression estimates the line of best fit through the data
Regression provides measures of association in units of the variable being measured
Correlation is the same as regression
The correlation coefficient is used to determine:
A specific value of the y-variable given a specific value of the x-variable
A specific value of the x-variable given a specific value of the y-variable
The strength of the relationship between the x and y variables
The estimation parameter
If there is a very strong correlation between two variables then the correlation coefficient must be:
any value larger than 1
much smaller than 0, if the correlation is negative
much larger than 0, regardless of whether the correlation is negative or positive
less than zero
In regression, the equation that describes how the response variable (y) is related to the explanatory variable (x) is:
the correlation model
the regression model
used to compute the correlation coefficient
the sample size
In regression analysis, the variable that is being predicted is the:
response, or dependent, variable
independent variable
intervening variable
is usually x
If two variables, x and y, have a very strong linear relationship, then:
there is evidence that x causes a change in y
there is evidence that y causes a change in x
there might not be any causal relationship between x and y
There is large correlation coefficient
If the coefficient of determination is equal to 1, then the correlation coefficient:
must also be equal to 1
can be either -1 or +1
can be any value between -1 to +1
must be -1
The coefficient of determination (sometimes known as the regression coefficient) enables you to:
assess whether two variables measure the same phenomenon.
measure the difference between two variables.
establish whether the data is telling you what you think it should tell you.
assess the strength of relationship between a quantifiable dependent variable and one or more quantifiable independent variables.
In regression analysis, if the independent variable is measured in kilograms, the dependent variable:
must also be in kilograms
must be in some unit of weight
cannot be in kilograms
can be any units
If the correlation coefficient is 0.8, the percentage of variation in the response variable explained by the variation in the explanatory variable is:
0.80%
80%
0.64%
64%
If the correlation coefficient is a positive value, then the slope of the regression line:
must also be positive
can be either negative or positive
can be zero
can not be zero
If the coefficient of determination is 0.81, the correlation coefficient:
is 0.6561
could be either + 0.9 or - 0.9
must be positive
must be negative
The coefficient of determination, r2, indicates:
The linear relationship between two variables
The slope of the line of best fit
How closely the data fit a defined curve
The sum of the residuals from each data point
Which of the following statements is true?
The coefficient of determination can have values from –1 to 1.
The coefficient of determination can have values from 1 to 2
The coefficient of determination can have values from –1 to -2.
The coefficient of determination can have values from 0 to 1.
Which of the following statements is false?
The coefficient of determination can have values from –1 to 1.
The coefficient of determination can be applied to any curve.
The coefficient of determination can be applied to any straight line.
The coefficient of determination is the variation in y explained by variation in x, divided by
the total variation in y.
Bivariate Data are the data collected for :
Two variables
More than two variables
Two variables at the same point of time
Two variables at different points of time.
Correlation analysis aims at :
Predicting one variable for a given value of the other variable
Establishing relation between two variables
Measuring the extent of relation between two variables
Investigate cause of outcome
Scatter diagram is considered for measuring :
Linear relationship between two variables
Curvilinear relationship between two variables
Predict dependent variable
Predict independent variable
If the plotted points in a scatter diagram lie from upper left to lower right, then the correlation is :
Positive
Zero
Negative
Strong
If the plotted points in a scatter diagram are evenly distributed, then the correlation is :
Zero
Negative
Positive
Weak
If all the plotted points in a scatter diagram lie on a single line, then the correlation is :
Perfect correlation
Perfect negative
Strong
Weak
Scatter diagram helps us to :
Find the nature correlation between two variables
Compute the extent of correlation between two variables
Obtain the mathematical relationship between two variables
Calculate slop
When correlation coefficient is 1, all the points in a scatter diagram would lie
On a straight line directed from lower left to upper right
On a straight line directed from upper left to lower right
On a straight line
Under the line
In descriptive statistics our main objective is to:
Describe the population
Describe the data we collected
Infer something about the population
Draw conclusion
Which of the following statements is true regarding a sample?
It is a part of population
It must contain at least five observations
It refers to descriptive statistics
It refers to estimation
A qualitative variable:
Always refers to a sample
Is not numeric
Has only two possible outcomes
Has many outcomes
A discrete variable is:
An example of a qualitative variable
Can assume only whole number values
Can assume only certain clearly separated values
Can have two values
Which of the following are examples of continuous variables?
Birth weight of babies
Distance between
Age in year
Number of children
Inferential statistics enable you to (Except one):
decide if the research hypothesis is true
decide if the null hypothesis is false
estimate population parameters.
decide if your research results are meaningful.
Inferential statistics enable you to :
decide if the research hypothesis is true
decide collecting data
estimate sample size
calculate sample size.
Inferential statistics enable you to :
decide conducting research
decide conducting interview
estimate population parameters.
decide analysis data
The mean of a sampling distribution of a sample statistic is called :
the mean of the means.
the standard error.
the central limit.
the expected value
Method is used to infer that the results from a sample are reflective of the true population scores.
Descriptive statistics
Regression statistics
Correlated statistics
Inferential statistics
The null hypothesis states the means are:
Equal
Not equal
Research hypothesis
Alternative hypothesis
A Type I error occurs when the null hypothesis is:
Rejected and the research hypothesis is actually false.
accepted but and research hypothesis is actually true.
rejected and null hypothesis is actually true.
accepted and null hypothesis is actually true.
Because of the possibility of error in sampling from populations, researchers use:
Unbiased
Significance
Probability
Descriptive
Inferential statistics are useful for:
Interviews
Observing natural behavior
Construct validity
Determining the probability of something
Which of the following statements regarding a researcher’s use of inferential statistics is true?
It is best to measure every member of a population if possible
We usually need to take several samples to obtain a good estimate of the population values.
Descriptive statistics from a sample are used to estimate the characteristics of the
population.
A random sample provides a perfect estimate of the population values.
A t-test is used to compare:
5 means
4 means
3 means
2 means
The two forms of t-test are:
One-way and two-way
Independent and dependent
Factorial and interaction
Bivariate and multiple
If a researcher conducts a study in which the reading ability of a class of 20 second graders is tested at the beginning and at the end of the year, the appropriate statistical procedure to analyze the results would be:
The dependent samples t-test
ANOVA
Chi-square
ANCOVA
What does it mean when you calculate a 95% confidence interval?
The process you used will capture the true parameter 95% of the time in the long run
You can be “95% confident” that your interval will include the population parameter
You can be “5% confident” that your interval will not include the population parameter
The sample result is between this interval
A procedure used to select a sample of n objects from a population in such a way that each member of the population is chosen strictly by chance, each member of the population is equally likely to be chosen, and every possible sample of a given size, n, has the same chance of selection is known as:
statistical thinking.
statistical analysis.
descriptive statistics.
Simple random sampling
Inferential statistics is a process that involves all of the following EXCEPT:
Estimating a parameter
Estimating a statistic.
test a hypothesis
analyze relationships
If a study is "reliable", this means that:
the methods are outlined in the methods discussion clearly enough for the research to be
replicated.
the measures devised for concepts are stable on different occasions.
the findings can be generalized to other social phenomena
it was conducted by a reputable researcher who can be trusted
Internal “validity" refers to:
Whether or not there is really a causal relationship between two variables
whether or not the findings are relevant to the researchers' everyday lives.
The extent to which the researcher believes that this was a worthwhile project
how accurately the measurements represent underlying concepts.
Which of the following requirements for a scientific report writing may depend on your institution (EXCEPT ONE)?
Whether an abstract should be included
The format for referencing
The size of the study
Oral presentation
An alternative to statistical techniques for analysis of data to find patterns and trends is
Data analysis
Data mining
Data mapping
Data trending
An example of an experimental study is a(n):
Randomized clinical trial
Cross-sectional study
Focus group
Case report
A scatter plot
Is used to compare parts to the whole or to compare the total with each part or percentage
Is useful in comparing differences in magnitude of different variables
Shows a continuous relationship between two variables over time
Shows a (negative or positive) relationship or correlation between 2 variables and how they
interact
The step in the data analysis process is (EXCEPT ONE):
Data analysis and interpretation
Data presentation
Data retrieval
Data promotion
The mean, median, mode and standard deviation are examples of:
Inferential statistics
Linear regression statistics
Descriptive statistics
Part of experimental studies
Which Statistical software come with appropriate table, graph, and/or chart-building features to easily convert tabular data into a variety of formats.
Excel
Access
Stata
Word, Excel
Quantitative data refers to:
any data you present in your report
graphs and tables.
numerical data that could usefully be quantified to help you answer your research question
(s) and to meet your objectives
statistical analysis
Which of these is not one of the four main reasons for missing data?
The respondent may have missed a question by mistake.
The respondent did not know the answer or did not have an opinion
The data was not required from the respondent, perhaps because of a skip generated by a
filter question in a survey
The analyst ignored its presence on the data form.
Computers are essential for quantitative data analysis because
they are so powerful
they are fun to use.
they enable easy calculation for those of us not too good with figures
increasingly data analysis software contain algorithms that check the data for obvious errors
as it is entered
Which part of written report that can answer the research question?
Introduction
Method
Result
Discussion
When the p-value is less than 0.05 we can conclude that:
Fail to reject the null hypothesis
No statistically significant
No evidence to reject the null hypothesis
Reject the null hypothesis
When the p-value is greater than 0.05 we can conclude that:
Reject the null hypothesis
Statistically significant
Strong evidence to reject the null hypothesis
Fail to reject the null hypothesis
To investigate the association between two categorical variables we use:
t-test
z-test
F-test
Chi-squared test
To compare two means we use:
Chi-squared test
Oneway ANOVA
F-test
t-test
To compare three independent means we use:
Chi-squared test
t-test
F-test
Oneway ANOVA
