AsterQuiz: Financial Management


AsterQuiz Financial ManagementOnline version Financial Management WGU by Theresa Leaverton 1 Data must be accurate and up-to-date a Quality Data b Big Data c Data Mining 2 Tool used to map choices and consequences a Decision Tree b Regression analysis c Control Chart 3 Used to compare costs and benefits a Cost-benefit analysis b Decision tree analysis c Regression analysis 4 Used by policymakers to forecast outcomes a Impact assessment b Cost-benefit analysis c Decision tree analysis 5 Predicts future outcomes using data a Predictive analytics b Prescriptive analytics c Descriptive analytics 6 Data arrives in real time a Velocity b Volume c Variety 7 Tweets, videos, emails, and logs a Variety b Velocity c Volume 8 Hidden patterns in historical data a Data Mining b Predictive Analytics c Data Management 9 Uses " IF-THEN" Rules a Decision Tree b Regression Analysis c Cost-Benefit Analysis 10 Fact-based decision-making a Quantitative Analysis b Qualitative Analysis c Impact Assessment 11 Zero means none a Ratio b Interval c Ordinal 12 Tweets, videos, emails a Variety b Velocity c Volume 13 Hidden patterns in historical data a Data Mining b Data Management c Descriptive Analytics 14 Categories with no order a Nominal b Ordinal c Interval 15 Data repeats consistently a Reliability b Validity c Accuracy 16 A blank field in a spreadsheet a Omission Error b Out-of-Range Error c Systematic Error 17 A birth year outside the allowed range. a Out-of-Range Error b Omission Error c Random Error 18 An error that fixes itself. a Random Error b Systematic Error c Omission Error 19 An error requiring intervention. a Systematic Error b Random Error c Out-of-Range Error 20 Data cleaning before analysis a Data Management b Data Mining c Predictive Analytics 21 Study with no treatment. a Observational Study b Experimental Study c Quasi-Experimental Study 22 Unit, treatment, response a Experimental Study b Observational Study c Blind Study 23 Participant doesn't know the treatment a Blind Study b Double-Blind Study c Observational Study 24 Sample too small a Measurement Bias b Information Bias c Response Bias 25 Question phrased to persuade a Conscious Bias b Response Bias c Information Bias 26 Participants don't know the treatment a Blind Study b Double-Blind Study c Triple-Blind Study 27 Participants and allocator are unaware a Double-Blind Study b Blind Study c Triple-Blind Study 28 All three parties are unaware a Triple-Blind Study b Double-Blind Study c Observational Study 29 Significant difference but no proven cause a Correlation vs. Causation b Experimental Design c Statistical Significance 30 Hidden variables influencing results a Research Limitations b Data Management c Measurement Reliability 31 Summarizes past and present data a Descriptive Analytics b Predictive Analytics c Prescriptive Analytics 32 Forecasts future outcomes a Predictive Analytics b Descriptive Analytics c Prescriptive Analytics 33 Optimizes decision using data a Prescriptive Analytics b Predictive Analytics c Descriptive Analytics 34 Pizza sizes ranked small, medium, and large. a Ordinal Data b Nominal Data c Interval Data 35 Delivery driver forgets to clock in a Omission Error b Out-of-Range Error c Systematic Error 36 Delivery distance is 20 miles (when 20 miles is outside the allowed range) a Out-of-Range Error b Omission Error c Random Error 37 Manager tastes pizzas without knowing the oil amount a Blind Study b Observational Study c Experimental Study 38 Sales per day a Ratio Data b Interval data c Ordinal Data 39 What builds a complete portrait of customers? a Statistics b Data Management c Experimental Design 40 A manufacturer wants to maximize output while minimizing labor costs a Prescriptive Analytics b Predictive Analytics c Descriptive Analytics 41 Which error is constant and caused by faulty equipment or bias? a Systematic Error b Random Error c Omission Error 42 Ohio students are used to represent national math skills. a Measurement Bias b Response Bias c Conscious Bias 43 Surveying immigrants in English only is an example of: a Omission Error b Out-of-Range Error c Random Error 44 Which is NOT true of Big Data? a Big Data can be analyzed with traditional spreadsheets b Big Data may contain structured and unstructured data c Big Data can involve large volumes of information 45 Framing the problem includes which two elements? a Determine Scope & Review Previous Findings b Collect Data & Perform Analysis c Develop Solutions & Implement Changes 46 Which are elements of an experimental study for glucose research? a Treatment Procedures & Experimental Response b Population Size & Data Mining c Reliability & Predictive Analytics 47 Marathon distance (26.2 miles) is what level of measurement? a Ratio b Interval c Ordinal 48 Types of cars produced in a ford factory represent what level of measurement? a Nominal b Ordinal c Ratio 49 A ranking of the 10 best cities to retire in represents: a Ordinal b Nominal c Interval 50 Women's dress sizes (0,2, 4, 6) represent: a Interval b Ratio c Nominal 51 Which study design helps eliminate bias from both students and teachers? a Double-Blind Study b Blind Study c Observational Study 52 Five cars are used to test wax durability. The cars are the: a Experimental Units b Treatments c Experimental Responses 53 A retailer assumes that increased December sales were caused by a newspaper ad. This illustrates: a Association vs. Causation b Reliability vs. Validity c Descriptive vs. Predictive Analytics 54 Using only 8th-grade scores to recommend curriculum for grades 8-12 is an example of: a Not a Representative Sample b Systematic Error c Out-of-Range Error 55 Predicting Y using one X. a Simple Regression b Multiple Regression c Logistic Regression 56 Trend over 30 years. a Time Series Regression b Simple Regression c Cluster Analysis 57 Avoid words like "Increase" or "impact" when describing a relationship. a Association vs. Causation b Reliability vs. Validity c Correlation Coefficient 58 H₀: m = 0 represents the: a Null Hypothesis b Alternative Hypothesis c Research Hypothesis 59 Click-through rate predicts weekly sales. a Regressioin Relationship b Time Series Relationship c Cluster Relationship 60 Predictor variable a Independent Variable b Dependent Variable c Control Variable 61 Outcome variable a Dependent Variable b Independent Variable c Predictor Variable 62 Explains 37% of variation. a R-Square b P-Value c Regression Coefficient 63 p-value < 0.05 a Statistical Significance b Statistical Reliability c Standard Deviation 64 Reject the null hypothesis a Hypothesis Testing b Descriptive Analysis c Data Management 65 Slope goes upward from left to right. a Positive Relationship b Negative Relationship c No Relationship 66 Coefficient m is negative a Negative Relationship b Positive Relationship c No Relationship 67 Y = 2662.2X + 3430. 84 a Linear Equation b Null Hypothesis c Correlation Coefficient 68 CTR explains 37% of weekly sales a R-Square (R²) b P-Value c Standard Deviation 69 CTR is significant (p < 0.05) a Reject the Null Hypothesis b Accept the Null Hypothesis c Fail to Reject the Null Hypothesis 70 Income predicts food insecurity. Income is the: a Independent Variable b Dependent Variable c Response Variable 71 p-value < 0.05 indicates: a Statistical Significance b Statistical Insignificance c Goodness of Fit 72 R-square = 0.43 is a measure of: a Goodness of Fit b Statistical Significance c Standard Error 73 Slope is negative a Negative Relationship b Positive Relationship c No Relationship 74 Only one year of data a Limitation b Statistical Significance c Regression Relationship 75 Observed value minus predicted value. a Residual b Standard Error c Regression Coefficient 76 Residuals must be squared a Sum of Squares b Standard Deviation c R-Square 77 Line that minimizes squared residuals a Line of Best Fit b Correlation Line c Standard Error Line 78 Method used in most regression models a OLS Regression b Time Series Analysis c Cluster Analysis 79 Measures scatter around the regression line. a Standard Error b R-Square c P-Value 80 One X predicts one Y a Linear Regression b Multiple Regression c Logistic Regression 81 Two predictors: Temperature + competitor sales a Multiple Regression b Linear Regression c Logistic Regression 82 Predicting a yes/no purchase. a Logistic Regression b Multiple Regression c Time Series Regression 83 Explains 29% of variation. a R-Square b P-Value c Standard Error 84 p-value = 6.91E-03 is written using: a Scientific Notation b Standard Deviation c Regression Notation 85 Ramp slope, skill level, wind direction, and board design are used together to predict an outcome. a Multiple Regression b Simple Linear Regression c Logistic Regression 86 β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + e. a Regression Equation b Correlation Equation c Null Hypothesis 87 Unexplained Variation a Error Term b Regression Coefficient c Independent Variable 88 Plane of best fit a Regression Visualization b Scatterplot c Time Series Visualization 89 Represents each X variable's effect while accounting for the other predictors. a Regression Coefficients b Residuals c R-Square 90 Demand decreases when price increases a Price Coefficient b Advertising Coefficient c Error Term 91 Binary outcome: 0 or 1 a Logistic Regression b Linear Regression c Multiple Regression 92 64% of variation is explained a R-Square b P-Value c Standard Error 93 p-value = 6.91E -03 is written using: a Scientific Notation b Regression Notation c Standard Notation 94 Multiple predictors: price + advertising a Multiple Regression b Simple Linear Regression c Logistic Regression 95 Predicts the probability of Y =1 a Logistic Regression b Linear Regression c Multiple Regression 96 Uses an S-Curve a Sigmoid Function b Linear Function c Exponential Function 97 Outcome is yes/no a Logistic Regression b Multinomial Logistic Regression c Ordered Logistic Regression 98 Outcome has three categories with no order a Multinomial Logistic Regression b Binomial Logistic Regression c Ordered Logistic Regression 99 Outcome has ordered categories a Ordered Logistic Regression b Multinomial Logistic Regression c Binomial Logistic Regression 100 Cone-shaped residual plot relates to: a Homoscedasticity b Normality c Linearity 101 Scatterplot looks curved. Which regression assumption should you check? a Linearity b Homoscedasticity c Normality 102 Extreme value outside the boxplot whiskers a Outlier b Residual c Regression Coefficient 103 Residuals follow a diagonal line in a Q-Q plot. a Normality b Linearity c Homoscedasticity 104 Multiple X variables weaken the usefulness of a single scatterplot. a Multiple Regression b Simple Regression c Logistic Regression 105 Yesterday's value strongly influences today's a Autocorrelation b Collinearity c Heteroscedasticity 106 Cone-shaped residual plot a Heteroscedasticity b Homoscedasticity c Autocorrelation 107 Two predictors are highly correlated. a Collinearity b Exogeneity c Normality 108 Binary Y variable used in OLS a Linear in Parameters b Homoscedasticity c Exogeneity 109 X is influenced by the error team a Exogeneity b Collinearity c Autocorrelation 110 Square node a Decision Node b Chance Node c Terminal Node 111 Circle Node a State of Nature b Decision Node c Terminal Node 112 Probability x payoff a Expected Value b Opportunity Cost c Maximum Payoff 113 Three alternatives: A, B, C a Decision Tree Question b Regression Question c Time Series Question 114 Risk environment a Decision-Making Under Risk b Decision-Making Under Certainty c Decision-Making Under Uncertainty 115 Demand X profit a Payoff b Expected Value c Probability 116 Probabilities must sum to 1 a State of Nature b Decision Alternative c Payoff 117 Three concerts: A, B, C a Decision Alternatives b States of Nature c Expected Values 118 Favorable probability = 1 - unfavorable probability. a Complement Probability b Conditional Probability c Joint Probability 119 Final value used to choose the best alternative a Expected Value b Payoff c State of Nature 120 No relationship exists. a Null Hypothesis b Alternative Hypothesis c Research Hypothesis 121 p-value < 0.05 a Significant Result b Insignificant Result c Type II Error 122 Opposite of the Null a Alternative Hypothesis b Null Hypothesis c Significance Level 123 Rejecting a true null hypothesis a Type I Error b Type II Error c Sampling Error 124 Failing to reject a false null hypothesis a Type II Error b Type I Error c Measurement Error 125 Probability of rejecting a true null hypothesis a Alpha (α) b Beta (β) c P-Value 126 Opposite of the null hypothesis a Alternative Hypothesis b Significance Level c Type I Error 127 Failing to reject a false null hypothesis a Type II Error b Type I Error c Sampling Error 128 Probability of obtaining extreme results if H₀ is true a p-value b Alpha (α) c Statistical Power 129 Power = 1 - β. a Power b Significance Level c Confidence Level 130 p < 0.05 a Statistical Significance b Statistical Power c Confidence Interval 131 Compare one mean to a target a One-Sample t-test b Two-Sample t-test c ANOVA 132 Compare two restaurants a Two-Sample t-Test b One-sample t-test c ANOVA 133 Compare four restaurants a ANOVA b Two-Sample t-Test c One-Sample t-Test 134 Opposite of the null a Alternative Hypothesis b Significance Level c Type I Error

1

Data must be accurate and up-to-date

a

Quality Data

b

Big Data

c

Data Mining

2

Tool used to map choices and consequences

a

Decision Tree

b

Regression analysis

c

Control Chart

3

Used to compare costs and benefits

a

Cost-benefit analysis

b

Decision tree analysis

c

Regression analysis

4

Used by policymakers to forecast outcomes

a

Impact assessment

b

Cost-benefit analysis

c

Decision tree analysis

5

Predicts future outcomes using data

a

Predictive analytics

b

Prescriptive analytics

c

Descriptive analytics

6

Data arrives in real time

a

Velocity

b

Volume

c

Variety

7

Tweets, videos, emails, and logs

a

Variety

b

Velocity

c

Volume

8

Hidden patterns in historical data

a

Data Mining

b

Predictive Analytics

c

Data Management

9

Uses " IF-THEN" Rules

a

Decision Tree

b

Regression Analysis

c

Cost-Benefit Analysis

10

Fact-based decision-making

a

Quantitative Analysis

b

Qualitative Analysis

c

Impact Assessment

11

Zero means none

a

Ratio

b

Interval

c

Ordinal

12

Tweets, videos, emails

a

Variety

b

Velocity

c

Volume

13

Hidden patterns in historical data

a

Data Mining

b

Data Management

c

Descriptive Analytics

14

Categories with no order

a

Nominal

b

Ordinal

c

Interval

15

Data repeats consistently

a

Reliability

b

Validity

c

Accuracy

16

A blank field in a spreadsheet

a

Omission Error

b

Out-of-Range Error

c

Systematic Error

17

A birth year outside the allowed range.

a

Out-of-Range Error

b

Omission Error

c

Random Error

18

An error that fixes itself.

a

Random Error

b

Systematic Error

c

Omission Error

19

An error requiring intervention.

a

Systematic Error

b

Random Error

c

Out-of-Range Error

20

Data cleaning before analysis

a

Data Management

b

Data Mining

c

Predictive Analytics

21

Study with no treatment.

a

Observational Study

b

Experimental Study

c

Quasi-Experimental Study

22

Unit, treatment, response

a

Experimental Study

b

Observational Study

c

Blind Study

23

Participant doesn't know the treatment

a

Blind Study

b

Double-Blind Study

c

Observational Study

24

Sample too small

a

Measurement Bias

b

Information Bias

c

Response Bias

25

Question phrased to persuade

a

Conscious Bias

b

Response Bias

c

Information Bias

26

Participants don't know the treatment

a

Blind Study

b

Double-Blind Study

c

Triple-Blind Study

27

Participants and allocator are unaware

a

Double-Blind Study

b

Blind Study

c

Triple-Blind Study

28

All three parties are unaware

a

Triple-Blind Study

b

Double-Blind Study

c

Observational Study

29

Significant difference but no proven cause

a

Correlation vs. Causation

b

Experimental Design

c

Statistical Significance

30

Hidden variables influencing results

a

Research Limitations

b

Data Management

c

Measurement Reliability

31

Summarizes past and present data

a

Descriptive Analytics

b

Predictive Analytics

c

Prescriptive Analytics

32

Forecasts future outcomes

a

Predictive Analytics

b

Descriptive Analytics

c

Prescriptive Analytics

33

Optimizes decision using data

a

Prescriptive Analytics

b

Predictive Analytics

c

Descriptive Analytics

34

Pizza sizes ranked small, medium, and large.

a

Ordinal Data

b

Nominal Data

c

Interval Data

35

Delivery driver forgets to clock in

a

Omission Error

b

Out-of-Range Error

c

Systematic Error

36

Delivery distance is 20 miles (when 20 miles is outside the allowed range)

a

Out-of-Range Error

b

Omission Error

c

Random Error

37

Manager tastes pizzas without knowing the oil amount

a

Blind Study

b

Observational Study

c

Experimental Study

38

Sales per day

a

Ratio Data

b

Interval data

c

Ordinal Data

39

What builds a complete portrait of customers?

a

Statistics

b

Data Management

c

Experimental Design

40

A manufacturer wants to maximize output while minimizing labor costs

a

Prescriptive Analytics

b

Predictive Analytics

c

Descriptive Analytics

41

Which error is constant and caused by faulty equipment or bias?

a

Systematic Error

b

Random Error

c

Omission Error

42

Ohio students are used to represent national math skills.

a

Measurement Bias

b

Response Bias

c

Conscious Bias

43

Surveying immigrants in English only is an example of:

a

Omission Error

b

Out-of-Range Error

c

Random Error

44

Which is NOT true of Big Data?

a

Big Data can be analyzed with traditional spreadsheets

b

Big Data may contain structured and unstructured data

c

Big Data can involve large volumes of information

45

Framing the problem includes which two elements?

a

Determine Scope & Review Previous Findings

b

Collect Data & Perform Analysis

c

Develop Solutions & Implement Changes

46

Which are elements of an experimental study for glucose research?

a

Treatment Procedures & Experimental Response

b

Population Size & Data Mining

c

Reliability & Predictive Analytics

47

Marathon distance (26.2 miles) is what level of measurement?

a

Ratio

b

Interval

c

Ordinal

48

Types of cars produced in a ford factory represent what level of measurement?

a

Nominal

b

Ordinal

c

Ratio

49

A ranking of the 10 best cities to retire in represents:

a

Ordinal

b

Nominal

c

Interval

50

Women's dress sizes (0,2, 4, 6) represent:

a

Interval

b

Ratio

c

Nominal

51

Which study design helps eliminate bias from both students and teachers?

a

Double-Blind Study

b

Blind Study

c

Observational Study

52

Five cars are used to test wax durability. The cars are the:

a

Experimental Units

b

Treatments

c

Experimental Responses

53

A retailer assumes that increased December sales were caused by a newspaper ad. This illustrates:

a

Association vs. Causation

b

Reliability vs. Validity

c

Descriptive vs. Predictive Analytics

54

Using only 8th-grade scores to recommend curriculum for grades 8-12 is an example of:

a

Not a Representative Sample

b

Systematic Error

c

Out-of-Range Error

55

Predicting Y using one X.

a

Simple Regression

b

Multiple Regression

c

Logistic Regression

56

Trend over 30 years.

a

Time Series Regression

b

Simple Regression

c

Cluster Analysis

57

Avoid words like "Increase" or "impact" when describing a relationship.

a

Association vs. Causation

b

Reliability vs. Validity

c

Correlation Coefficient

58

H₀: m = 0 represents the:

a

Null Hypothesis

b

Alternative Hypothesis

c

Research Hypothesis

59

Click-through rate predicts weekly sales.

a

Regressioin Relationship

b

Time Series Relationship

c

Cluster Relationship

60

Predictor variable

a

Independent Variable

b

Dependent Variable

c

Control Variable

61

Outcome variable

a

Dependent Variable

b

Independent Variable

c

Predictor Variable

62

Explains 37% of variation.

a

R-Square

b

P-Value

c

Regression Coefficient

63

p-value < 0.05

a

Statistical Significance

b

Statistical Reliability

c

Standard Deviation

64

Reject the null hypothesis

a

Hypothesis Testing

b

Descriptive Analysis

c

Data Management

65

Slope goes upward from left to right.

a

Positive Relationship

b

Negative Relationship

c

No Relationship

66

Coefficient m is negative

a

Negative Relationship

b

Positive Relationship

c

No Relationship

67

Y = 2662.2X + 3430. 84

a

Linear Equation

b

Null Hypothesis

c

Correlation Coefficient

68

CTR explains 37% of weekly sales

a

R-Square (R²)

b

P-Value

c

Standard Deviation

69

CTR is significant (p < 0.05)

a

Reject the Null Hypothesis

b

Accept the Null Hypothesis

c

Fail to Reject the Null Hypothesis

70

Income predicts food insecurity. Income is the:

a

Independent Variable

b

Dependent Variable

c

Response Variable

71

p-value < 0.05 indicates:

a

Statistical Significance

b

Statistical Insignificance

c

Goodness of Fit

72

R-square = 0.43 is a measure of:

a

Goodness of Fit

b

Statistical Significance

c

Standard Error

73

Slope is negative

a

Negative Relationship

b

Positive Relationship

c

No Relationship

74

Only one year of data

a

Limitation

b

Statistical Significance

c

Regression Relationship

75

Observed value minus predicted value.

a

Residual

b

Standard Error

c

Regression Coefficient

76

Residuals must be squared

a

Sum of Squares

b

Standard Deviation

c

R-Square

77

Line that minimizes squared residuals

a

Line of Best Fit

b

Correlation Line

c

Standard Error Line

78

Method used in most regression models

a

OLS Regression

b

Time Series Analysis

c

Cluster Analysis

79

Measures scatter around the regression line.

a

Standard Error

b

R-Square

c

P-Value

80

One X predicts one Y

a

Linear Regression

b

Multiple Regression

c

Logistic Regression

81

Two predictors: Temperature + competitor sales

a

Multiple Regression

b

Linear Regression

c

Logistic Regression

82

Predicting a yes/no purchase.

a

Logistic Regression

b

Multiple Regression

c

Time Series Regression

83

Explains 29% of variation.

a

R-Square

b

P-Value

c

Standard Error

84

p-value = 6.91E-03 is written using:

a

Scientific Notation

b

Standard Deviation

c

Regression Notation

85

Ramp slope, skill level, wind direction, and board design are used together to predict an outcome.

a

Multiple Regression

b

Simple Linear Regression

c

Logistic Regression

86

β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + e.

a

Regression Equation

b

Correlation Equation

c

Null Hypothesis

87

Unexplained Variation

a

Error Term

b

Regression Coefficient

c

Independent Variable

88

Plane of best fit

a

Regression Visualization

b

Scatterplot

c

Time Series Visualization

89

Represents each X variable's effect while accounting for the other predictors.

a

Regression Coefficients

b

Residuals

c

R-Square

90

Demand decreases when price increases

a

Price Coefficient

b

Advertising Coefficient

c

Error Term

91

Binary outcome: 0 or 1

a

Logistic Regression

b

Linear Regression

c

Multiple Regression

92

64% of variation is explained

a

R-Square

b

P-Value

c

Standard Error

93

p-value = 6.91E -03 is written using:

a

Scientific Notation

b

Regression Notation

c

Standard Notation

94

Multiple predictors: price + advertising

a

Multiple Regression

b

Simple Linear Regression

c

Logistic Regression

95

Predicts the probability of Y =1

a

Logistic Regression

b

Linear Regression

c

Multiple Regression

96

Uses an S-Curve

a

Sigmoid Function

b

Linear Function

c

Exponential Function

97

Outcome is yes/no

a

Logistic Regression

b

Multinomial Logistic Regression

c

Ordered Logistic Regression

98

Outcome has three categories with no order

a

Multinomial Logistic Regression

b

Binomial Logistic Regression

c

Ordered Logistic Regression

99

Outcome has ordered categories

a

Ordered Logistic Regression

b

Multinomial Logistic Regression

c

Binomial Logistic Regression

100

Cone-shaped residual plot relates to:

a

Homoscedasticity

b

Normality

c

Linearity

101

Scatterplot looks curved. Which regression assumption should you check?

a

Linearity

b

Homoscedasticity

c

Normality

102

Extreme value outside the boxplot whiskers

a

Outlier

b

Residual

c

Regression Coefficient

103

Residuals follow a diagonal line in a Q-Q plot.

a

Normality

b

Linearity

c

Homoscedasticity

104

Multiple X variables weaken the usefulness of a single scatterplot.

a

Multiple Regression

b

Simple Regression

c

Logistic Regression

105

Yesterday's value strongly influences today's

a

Autocorrelation

b

Collinearity

c

Heteroscedasticity

106

Cone-shaped residual plot

a

Heteroscedasticity

b

Homoscedasticity

c

Autocorrelation

107

Two predictors are highly correlated.

a

Collinearity

b

Exogeneity

c

Normality

108

Binary Y variable used in OLS

a

Linear in Parameters

b

Homoscedasticity

c

Exogeneity

109

X is influenced by the error team

a

Exogeneity

b

Collinearity

c

Autocorrelation

110

Square node

a

Decision Node

b

Chance Node

c

Terminal Node

111

Circle Node

a

State of Nature

b

Decision Node

c

Terminal Node

112

Probability x payoff

a

Expected Value

b

Opportunity Cost

c

Maximum Payoff

113

Three alternatives: A, B, C

a

Decision Tree Question

b

Regression Question

c

Time Series Question

114

Risk environment

a

Decision-Making Under Risk

b

Decision-Making Under Certainty

c

Decision-Making Under Uncertainty

115

Demand X profit

a

Payoff

b

Expected Value

c

Probability

116

Probabilities must sum to 1

a

State of Nature

b

Decision Alternative

c

Payoff

117

Three concerts: A, B, C

a

Decision Alternatives

b

States of Nature

c

Expected Values

118

Favorable probability = 1 - unfavorable probability.

a

Complement Probability

b

Conditional Probability

c

Joint Probability

119

Final value used to choose the best alternative

a

Expected Value

b

Payoff

c

State of Nature

120

No relationship exists.

a

Null Hypothesis

b

Alternative Hypothesis

c

Research Hypothesis

121

p-value < 0.05

a

Significant Result

b

Insignificant Result

c

Type II Error

122

Opposite of the Null

a

Alternative Hypothesis

b

Null Hypothesis

c

Significance Level

123

Rejecting a true null hypothesis

a

Type I Error

b

Type II Error

c

Sampling Error

124

Failing to reject a false null hypothesis

a

Type II Error

b

Type I Error

c

Measurement Error

125

Probability of rejecting a true null hypothesis

a

Alpha (α)

b

Beta (β)

c

P-Value

126

Opposite of the null hypothesis

a

Alternative Hypothesis

b

Significance Level

c

Type I Error

127

Failing to reject a false null hypothesis

a

Type II Error

b

Type I Error

c

Sampling Error

128

Probability of obtaining extreme results if H₀ is true

a

p-value

b

Alpha (α)

c

Statistical Power

129

Power = 1 - β.

a

Power

b

Significance Level

c

Confidence Level

130

p < 0.05

a

Statistical Significance

b

Statistical Power

c

Confidence Interval

131

Compare one mean to a target

a

One-Sample t-test

b

Two-Sample t-test

c

ANOVA

132

Compare two restaurants

a

Two-Sample t-Test

b

One-sample t-test

c

ANOVA

133

Compare four restaurants

a

ANOVA

b

Two-Sample t-Test

c

One-Sample t-Test

134

Opposite of the null

a

Alternative Hypothesis

b

Significance Level

c

Type I Error

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	Anonymous	%
		%
		%

Financial Management

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AsterQuiz

Financial ManagementOnline version

by Theresa Leaverton

Data must be accurate and up-to-date

Tool used to map choices and consequences

Used to compare costs and benefits

Used by policymakers to forecast outcomes

Predicts future outcomes using data

Data arrives in real time

Tweets, videos, emails, and logs

Hidden patterns in historical data

Uses " IF-THEN" Rules

Fact-based decision-making

Zero means none

Tweets, videos, emails

Hidden patterns in historical data

Categories with no order

Data repeats consistently

A blank field in a spreadsheet

A birth year outside the allowed range.

An error that fixes itself.

An error requiring intervention.

Data cleaning before analysis

Study with no treatment.

Unit, treatment, response

Participant doesn't know the treatment

Sample too small

Question phrased to persuade

Participants don't know the treatment

Participants and allocator are unaware

All three parties are unaware

Significant difference but no proven cause

Hidden variables influencing results

Summarizes past and present data

Forecasts future outcomes

Optimizes decision using data

Pizza sizes ranked small, medium, and large.

Delivery driver forgets to clock in

Delivery distance is 20 miles (when 20 miles is outside the allowed range)

Manager tastes pizzas without knowing the oil amount

Sales per day

What builds a complete portrait of customers?

A manufacturer wants to maximize output while minimizing labor costs

Which error is constant and caused by faulty equipment or bias?

Ohio students are used to represent national math skills.

Surveying immigrants in English only is an example of:

Which is NOT true of Big Data?

Framing the problem includes which two elements?

Which are elements of an experimental study for glucose research?

Marathon distance (26.2 miles) is what level of measurement?

Types of cars produced in a ford factory represent what level of measurement?

A ranking of the 10 best cities to retire in represents:

Women's dress sizes (0,2, 4, 6) represent:

Which study design helps eliminate bias from both students and teachers?

Five cars are used to test wax durability. The cars are the:

A retailer assumes that increased December sales were caused by a newspaper ad. This illustrates:

Using only 8th-grade scores to recommend curriculum for grades 8-12 is an example of:

Predicting Y using one X.

Trend over 30 years.

Avoid words like "Increase" or "impact" when describing a relationship.

H₀: m = 0 represents the:

Click-through rate predicts weekly sales.

Predictor variable

Outcome variable

Explains 37% of variation.

p-value < 0.05

Reject the null hypothesis

Slope goes upward from left to right.

Coefficient m is negative

Y = 2662.2X + 3430. 84

CTR explains 37% of weekly sales

CTR is significant (p < 0.05)

Income predicts food insecurity. Income is the:

p-value < 0.05 indicates: