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GSDSA Quiz on Predictive Analytics

Total questions: 50

Worksheet time: 38mins

Name
Class
Date
1.

Predictive analytics mainly uses ______ to forecast future events.

a)

Assumptions

b)

Historical data

c)

Random numbers

d)

Surveys

2.

Which of the following is NOT a key concept of predictive analytics?

a)

Simple Linear Regression

b)

Multiple Regression

c)

Polynomial Regression

d)

T-test

3.

In predictive analytics, the process of cleaning and preparing data is called:

a)

Data evaluation

b)

Data mining

c)

Data preparation

d)

Model deployment

4.

Which industry uses predictive analytics to detect fraud?

a)

Education

b)

Retail

c)

Financial Services

d)

Manufacturing

5.

Predictive analytics in healthcare is commonly used to:

a)

Write medical reports

b)

Identify high-risk patients

c)

Increase medicine prices

d)

Assign doctors

6.

Which step comes after model building?

a)

Data cleaning

b)

Model evaluation

c)

Predictive charting

d)

Visualization

7.

Which question can predictive analytics answer?

a)

Why do customers complain?

b)

Which customers are likely to churn?

c)

What is the company mission?

d)

Who is the CEO?

8.

A major challenge of predictive analytics is:

a)

Too many analysts

b)

Incomplete and inaccurate data

c)

High employee turnover

d)

Too much profit

9.

Predictive analytics improves business by:

a)

Increasing inventory waste

b)

Removing customers

c)

Improving decision making

d)

Blocking databases

10.

A predictive model becomes biased when:

a)

Data is accurate

b)

Model is transparent

c)

Training data contains bias

d)

Variables are strong

11.

Simple linear regression analyzes the relationship between:

a)

Two variables

b)

Three variables

c)

Multiple variables

d)

No variables

12.

In regression, “x” is the:

a)

Dependent variable

b)

Intercept

c)

Independent variable

d)

Residual

13.

The regression formula is:

a)

y=abxy = abx

b)

y=bx+ay = bx + a

c)

y=x+b2y = x + b^2

d)

y=a/xy = a/x

14.

Residuals represent

a)

Predicted values

b)

Errors

c)

Intercepts

d)

Correct outputs

15.

Residuals with curved patterns indicate

a)

Regression is correct

b)

Linear model might not fit

c)

Regression equation is perfect

d)

Data is random

16.

A fan-shaped residual pattern indicates

a)

Heteroscedasticity

b)

Normality

c)

Homoscedasticity

d)

Linearity

17.

An extreme value that influences the regression line is

a)

Mean

b)

Median

c)

Outlier

d)

Mode

18.

In Excel regression, the slope is found in the

a)

ANOVA table

b)

Coefficients table

c)

Residual table

d)

Summary tool

19.

R-square measures

a)

How curved the model is

b)

How much variation in y is explained by x

c)

Number of variables

d)

Data size

20.

The F-test in ANOVA tests

a)

If the model is significant

b)

If x=yx = y

c)

Data size

d)

Trend slope

21.

Multiple regression uses

a)

One dependent and one independent variable

b)

Several dependent variables

c)

One dependent, many independent variables

d)

No variables

22.

In multiple regression, the dependent variable is the one being

a)

Predicted

b)

Counted

c)

Stored

d)

Deleted

23.

The formula for MLR is

a)

y=axy = ax

b)

y=b1x1+b2x2+⋯+ay = b_1 x_1 + b_2 x_2 + \dots + a

c)

y=x/yy = x / y

d)

y=x1+x2y = x_1 + x_2

24.

A variable is significant when its p-value is

a)

>0.10> 0.10

b)

<0.05< 0.05

c)

>1> 1

d)

=100= 100

25.

MLR is useful when analyzing

a)

A single factor

b)

Multiple predictors

c)

Only categorical variables

d)

Random noise

26.

An example of a dependent variable in education

a)

Hours of study

b)

Student performance

c)

Number of seats

d)

Type of classroom

27.

Independent variables in the TV show example include

a)

Ratings

b)

Viewers’ engagement

c)

Production cost

d)

Network name

28.

The error term ( ϵ\epsilon ) represents:

a)

Perfect predictions

b)

Unexplained variation

c)

The slope

d)

The intercept

29.

When a model includes unnecessary variables, it may suffer from:

a)

Underfitting

b)

Perfect accuracy

c)

Overfitting

d)

No bias

30.

In Excel MLR, which must be checked?

a)

Fonts

b)

Border colors

c)

P-values

d)

Cell color

31.

Polynomial regression is used for relationships that are:

a)

Straight

b)

Nonlinear

c)

Constant

d)

Random

32.

Polynomial equation includes terms such as:

a)

x1x^1 only

b)

x2x^2 , x3x^3

c)

x−1x^{-1}

d)

log⁡(x)\log(x)

33.

Adding x2x^2 creates a:

a)

Linear model

b)

Quadratic model

c)

Logistic model

d)

Exponential model

34.

A very high polynomial degree may cause:

a)

Perfect interpretation

b)

Overfitting

c)

Simplicity

d)

No residuals

35.

In Excel, polynomial regression uses:

a)

Pie chart

b)

Polynomial trendline

c)

Frequency table

d)

Histogram

36.

R2R^2 tells us the:

a)

Trend angle

b)

Goodness of fit

c)

Chart color

d)

Number of records

37.

When teaching hours increase then decrease motivation, the pattern is:

a)

Linear

b)

Constant

c)

Curvilinear

d)

Random

38.

Overfitting occurs when the model:

a)

Fits the general trend

b)

Fits noise too closely

c)

Uses correct order

d)

Has simple variables

39.

Logistic regression predicts:

a)

Continuous values

b)

Binary outcomes

c)

Colors

d)

Shapes

40.

The logistic regression curve is:

a)

Straight

b)

S-shaped

c)

Circular

d)

Downward

41.

Which is a valid example of logistic regression?

a)

Predicting grades

b)

Predicting pass/fail

c)

Predicting height

d)

Forecasting sales

42.

Logistic regression estimates:

a)

Probability of an event

b)

Number of events

c)

Cost

d)

Weight

43.

Logistic regression in Excel requires:

a)

Sorting

b)

Solver

c)

Pivot table

d)

Conditional formatting

44.

In logistic regression, a higher number of practice essays increases:

a)

Costs

b)

Probability of passing

c)

Uncertainty

d)

Randomness

45.

Independent variables in logistic regression may include:

a)

Intercepts

b)

Probabilities

c)

Hours studied

d)

Sig. values

46.

The TREND function returns:

a)

Only future values

b)

Linear trend values

c)

Shapes

d)

Colors

47.

FORECAST function predicts:

a)

Past values

b)

Future values

c)

Intercepts only

d)

Trends only

48.

The LINEST function returns:

a)

Regression statistics

b)

Colors

c)

Shapes

d)

Pivot tables

49.

TREND must be entered as:

a)

Regular formula

b)

Array formula

c)

Chart label

d)

Macro

50.

FORECAST and TREND produce the same values when performing:

a)

Nonlinear modeling

b)

Linear forecasting

c)

Logistic regression

d)

Random projection

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