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IDIS 450 > Exam 2 > Flashcards

Exam 2 Flashcards

1
Q

What is the main assumption of a sample linear regression model?

A

The relationship between dependent and independent variables is linear.

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2
Q

In simple linear regression, what does the slope of the regression line represent?

A

The change in the dependent variable for a one-unit change in the independent variable.

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3
Q

What is the objective of the least squares method in simple linear regression?

A

To minimize the sum of squared differences between observed and predicted values. (residuals)

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4
Q

In the equation Y = B0 + B1X + eY, what does the e/epsilon represent?

A

The residual or error term.

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5
Q

In multiple linear regression, what is a key difference compared to simple linear regression?

A

There is more than one independent variable.

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6
Q

What is multicollinearity in multiple linear regression?

A

When independent variables are highly correlated with each other

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7
Q

How can you reduce multicollinearity in multiple linear regression?

A

Remove or combine highly correlated independent variables.

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8
Q

In multiple linear regression, what does the adjusted R-squared value represent?

A

The percentage of total variation explained by the model, adjusted for the number of predictors

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9
Q

When performing multiple linear regression, why is it important to check residual plots?

A

To check for homoscedasticity and ensure residuals are randomly distributed

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10
Q

In the context of industrial distribution, if we want to predict distribution costs based on distance, product weight, and delivery method, what type of regression would be appropriate?

A

Multiple Linear Regression

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11
Q

What type of dependent variable is logistic regression used for?

A

Categorical Variables

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12
Q

In binary logistic regression, what does the odds ratio represent?

A

The change in odds of the dependent variable being 1 for a one-unit increase in independent variable

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13
Q

In logistic regression, which function is used to transform the linear combination of inputs into a probability?

A

Sigmoid Function

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14
Q

Which of the following is NOT an assumption of logistic regression?

A

The residuals must be normally distributed.

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15
Q

What is the role of the activation function in a neuron of an artificial neural network?

A

To introduce non-linearity into the model and determine if the neuron should be activated.

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16
Q

In a feed-forward neural network, information flows in which direction?

A

Forward from the input layer to the output layer

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17
Q

In the backpropagation process in an artificial neural network, which of the following is true?

A

The weights are adjusted to minimize the loss function by calculating gradients

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18
Q

Which of the following is NOT a common activation function used in artificial neural networks?

A

Softmax

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19
Q

What is logistic regression used for?

A

Binary values

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20
Q

What are Residuals?

A

the observed errors associated with estimating the value of the dependent variable using the regression line.

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21
Q

Residual Equation

A

Actual Y value - Predicted Y value

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22
Q

Standard Residual Equation

A

residual / standard deviation

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23
Q

What are the four assumptions of linear regressions?

A
  • Linearity
  • Normality of Errors
  • Homoscedasticity
  • Independence of Errors
24
Q

Explanatory Variables are the same as…

A

Independent variables and x-variables

25
Q

Response Variables are the same as…

A

Dependent variables, Y-variables, & outcome variables

26
Q

What do you look at to determine if a variable is statistically insignificant?

A

Look at p-values greater than alpha

27
Q

What is overfitting?

A

Fitting a model too closely to the sample data at the risk of not fitting it well to the population in which we are interested

28
Q

Equation of a Logit model in terms of probability (p)

A

p = 1 / 1+e^-(regression equation)

29
Q

The Odds of an event are defined as…

A

Odds = p / (1-p)

30
Q

Given the odds of an event, the probability of is defined as…

A

odds / (1+odds)

31
Q

What are the three equivalent logit equations?

A

logit = ln(odds) = ln(p/(1-p)) = B0+B1X

32
Q

What is Binary Logistic Regression?

A

The categorical response has only two possible outcomes. Example: Spam or Not

33
Q

What is Multinomial Logistic Regression

A

Three or more categories without ordering. example: predicting which food is preferred more (Veg, Non-Veg, Vegan)

34
Q

What is Ordinal Logistic Regression?

A

Three or more categories with ordering. Example: move rating from 1 to 5

35
Q

What are the 7 application areas of ANN?

A
  • Science and Medicine
  • Manufacturing
  • Marketing and Sales
  • Finance
  • Banking and Insurance
  • Security
  • Engineering
36
Q

What is Machine Learning (ML)?

A

Type of algorithm that automatically improves itself based on experience, not by a programmer writing a better algorithm.

37
Q

What are the three types of machine learning?

A
  • Reinforcement Learning
  • Unsupervised Machine Learning
  • Supervised Machine Learning
38
Q

An increasingly popular approach to supervised machine learning is…

A

the neural network

39
Q

What is an Artificial Neural Network (ANN)?

A

computational model that is inspired by the way biological neural networks in the human brain process information

40
Q

What is the goal of the training phase for an ANN?

A

to determine weights for connections that will correctly classify the training data

41
Q

the weight control the ____ __ _______

A

strength of influence

42
Q

We model the firing rate of the neuron with…

A

an activation function f

43
Q

Behavior of an artificial neural network to any particular input depends upon: (3)

A
  • structure of each node (activation function)
  • structure of the network (architecture)
  • weights on each of the connections
44
Q

Is an activation function linear or non-linear?

A

Non-linear

45
Q

Why are activation functions important?

A

most real-world data is nonlinear and we want neurons to learn these nonlinear representations

46
Q

Sigmoid Activation Function

A

Takes a real-valued input and squashes it to a range between 0 and 1

1 / (1+exp(-x))

47
Q

Tanh Activation Function

A

Takes a real-valued input and squashes it to the range [-1,1]

2o(2x)-1

48
Q

ReLU Activation Function

A

Stands for Rectified Linear Unit. It takes a real-valued input and thresholds it at zero (replaces negative values with zero)

f(x) = max(0, x)

49
Q

What is the main function of Bias?

A

to provide every node with a trainable constant value

50
Q

A bias allows you to shift the _______ _______ to the left or right.

A

activation function

51
Q

Bias helps in ____ ___ ____ at which activation function will trigger

A

controlling the value

52
Q

A negative bias shifts the graph which way?

A

to the right

53
Q

Feedforward Neural Networks learn though….

A

backpropagation

54
Q

Input Nodes

A

No computation is performed in any of the input nodes

55
Q

Hidden Nodes

A

While a feedforward network will only have a single input layer and a single output layer, it can have zero or multiple hidden layers.

56
Q

Output Nodes

A

Responsible for computations and transferring information

57
Q

What is Backpropagation?

A

The process in which a multilayer perceptron learns

IDIS 450 (3 decks)

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