# Business Analytics at TU München

## Flashcards and summaries for Business Analytics at the TU München

It’s completely free

4.5 /5

4.8 /5

4.5 /5

4.8 /5

## Study with flashcards and summaries for the course Business Analytics at the TU München

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

Gauss markov and unbiased, consistent and efficient estimators

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

5) Expected value of the residual vector, given 𝑋, is 0 (𝐸 𝜀 𝑋 = 0)

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

What are the three main estimators in the fixed-effect Model?

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

What is the random effects assumption?

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

1) linearity+ reformulations

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

Numerical Prediction

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

Association Rule Analysis

Classification

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

What are the steps from data to information?

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

What is the fixed effect assumption?

Clustering

### Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

For an algorithm to be useful in a wide range of real-world
applications it must:

• Basic algorithm needs to be extended to fulfill these requirements

Your peers in the course Business Analytics at the TU München create and share summaries, flashcards, study plans and other learning materials with the intelligent StudySmarter learning app.

Get started now!

## Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

Gauss markov and unbiased, consistent and efficient estimators

Unbiased, if ß^=ß,

Consist-ent, if var down, with n up

efficient, if no other linear estimates better

Gauss markov assumptions

1) linearity

2) no multicollinearity amongst predictors

3) Homoskedacity

4) No autocorrelation

5) Expected value of residual = 0

5) Expected value of the residual vector, given 𝑋, is 0 (𝐸 𝜀 𝑋 = 0)

Assumption: Other factors, which are not explicitely accounted for in the
model but are contained in 𝜀, are not correlated with 𝑋 (exogeneity)

• Endogeneity is given when an independent variable is correlated with the
error term and the covariance is not null

–> Probably omitted variable bias

What are the three main estimators in the fixed-effect Model?

1) First-differences (most elegant way)
2) Within

3) Between
4) Least squares dummy variable (disadvantage of keeping track of many dummy variables)

What is the random effects assumption?

The random effects assumption (in a random effects model) is that the
individual specific effects are uncorrelated with the independent variables
(𝑐𝑜𝑣 𝜆𝑖, 𝑥𝑗𝑖𝑡 = 0, but 𝜆𝑖 might be correlated).

1) linearity+ reformulations

If not applicable –> reformulate

1) polynomial regressions (if curve in data)

2) transform log if outliers

3) non linear with constant (ex Experten(..) if curve, but no negative turn

4) piecewise

Numerical Prediction

• Given a collection of data with known numeric outputs, create a function that outputs a
predicted value from a new set of inputs
• E.g. given gestation time of an animal, predict its maximum life span

Association Rule Analysis

• Identify relationships in data from co-occuring terms or items
• E.g., analyze grocery store purchases to identify items most commonly purchased together

Classification

• From data with known labels, create a classifier that determines which label to apply to a
new observation
• E.g. Identify new loan applicants as low, medium, or high risk based on existing applicant
behavior

What are the steps from data to information?
1. Data consolidation
2. Selection and Processing
3. Predictive Analytics
4. Interpretation and Evaluation

What is the fixed effect assumption?

The fixed effect assumption is that the individual specific effect is
correlated with the independent variables (𝑐𝑜𝑣 𝜆𝑖, 𝑥𝑗𝑖𝑡 ≠ 0).

Clustering

• Identify “natural” groupings in data
• Unsupervised learning, no predefined groups
• E.g. Identify clusters of “similar” customers

For an algorithm to be useful in a wide range of real-world
applications it must:

• Basic algorithm needs to be extended to fulfill these requirements

– Permit numeric attributes
– Allow missing values
– Be robust in the presence of noise

• Basic algorithm needs to be extended to fulfill these requirements

## Other courses from your degree program

For your degree program Computer Science at the TU München there are already many courses on StudySmarter, waiting for you to join them. Get access to flashcards, summaries, and much more.

Back to TU München overview page

## What is StudySmarter?

StudySmarter is an intelligent learning tool for students. With StudySmarter you can easily and efficiently create flashcards, summaries, mind maps, study plans and more. Create your own flashcards e.g. for Business Analytics at the TU München or access thousands of learning materials created by your fellow students. Whether at your own university or at other universities. Hundreds of thousands of students use StudySmarter to efficiently prepare for their exams. Available on the Web, Android & iOS. It’s completely free.

Best EdTech Startup in Europe

EUROPEAN YOUTH AWARD IN SMART LEARNING

BEST EDTECH STARTUP IN GERMANY

Best EdTech Startup in Europe

EUROPEAN YOUTH AWARD IN SMART LEARNING

BEST EDTECH STARTUP IN GERMANY

## How it works

### Get a learning plan

Prepare for all of your exams in time. StudySmarter creates your individual learning plan, tailored to your study type and preferences.

### Create flashcards

Create flashcards within seconds with the help of efficient screenshot and marking features. Maximize your comprehension with our intelligent StudySmarter Trainer.

### Create summaries

Highlight the most important passages in your learning materials and StudySmarter will create a summary for you. No additional effort required.

### Study alone or in a group

StudySmarter automatically finds you a study group. Share flashcards and summaries with your fellow students and get answers to your questions.

### Statistics and feedback

Always keep track of your study progress. StudySmarter shows you exactly what you have achieved and what you need to review to achieve your dream grades.

1

2

3

4

5