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Lernmaterialien für Statistics an der Ariel University Center of Samaria

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TESTE DEIN WISSEN

Define expected value

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TESTE DEIN WISSEN

The expected value of a random variable is the sum of all the values of the random variable multiplied by their probabilities.

 

Jenny = 1, Lora = 2, Sanya = 3

1 = 0.1

2 =  0.5

3 = 0.3

expected_one_to_call = 1*0.1 + 2*0.5 + 3*0.3 = 2 

Our expected value is 2, (Lora)


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TESTE DEIN WISSEN

The solution for graphical represantation of discrete variables is histogram. But that doesn't work for the continues variables. What are the solutions?

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TESTE DEIN WISSEN

One way to visualize the distribution of continuous variables is to divide the set of possible values into intervals and count the number of values in each interval.

When plotting a histogram in pandas, you can set the number of intervals (bins) and set their boundaries explicitly:

data.hist( bins = [value1, value2, value.... valueN])

To overcome the difficulties of creating a histogram for a continuous variable, we can use a slightly different technique that represents frequency not as the height of a column, but as its area (the length of the interval times the height of the column). This area is the frequency of the continuous variable, and the height of the column is the frequency density. A histogram that uses frequency density is called a density histogram.  To estimate how many values fall in a particular interval, take two values and find the total area of the density histograms between them. The number you get will be an estimate of the number of values in that interval. 

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Data with negative skew (skewed to the left) has ...


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TESTE DEIN WISSEN

Data with negative skew (skewed to the left) has a mean that is less than the median. The data will have more values below the mean than above it.

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TESTE DEIN WISSEN

How do you call the std func in python. What library do you use?

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standard_deviation = np.std(dataset)

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Data with positive skew (skewed to the right) has... 


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TESTE DEIN WISSEN

Data with positive skew (skewed to the right) has a mean that is greater than the median. The data will have more values greater than the mean than below it.

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TESTE DEIN WISSEN

Find dispersion of data

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TESTE DEIN WISSEN

import numpy as np 

np.var(data) 

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What is Frequency density ?

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TESTE DEIN WISSEN

Frequency density — a value equal to the height of a histogram column whose area reflects the relative frequency of a continuous variable.

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TESTE DEIN WISSEN

Define a discrete variable.

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TESTE DEIN WISSEN

 A  discrete variable is any variable that is not continuous on any range (for example a variable that takes the integer values from 0 to 100). 

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What is a Density histogram 

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TESTE DEIN WISSEN

Density histogram — a histogram that uses frequency density. 

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TESTE DEIN WISSEN

What is μ?

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TESTE DEIN WISSEN

The mean, often denoted by the Greek letter mu, μ. 

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TESTE DEIN WISSEN

Build histograms of data with set interval boundaries 16,18,20,58,100


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TESTE DEIN WISSEN

data.hist(bins=[16,18,20,58,100 ]) 

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TESTE DEIN WISSEN

If mean > median 

is it a positive or negative skew?

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TESTE DEIN WISSEN

if the mean is greater than the median, then the data has positive skew. Similarly, if the mean is less than the median, then the data has negative skew. 

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Q:

Define expected value

A:

The expected value of a random variable is the sum of all the values of the random variable multiplied by their probabilities.

 

Jenny = 1, Lora = 2, Sanya = 3

1 = 0.1

2 =  0.5

3 = 0.3

expected_one_to_call = 1*0.1 + 2*0.5 + 3*0.3 = 2 

Our expected value is 2, (Lora)


Q:

The solution for graphical represantation of discrete variables is histogram. But that doesn't work for the continues variables. What are the solutions?

A:

One way to visualize the distribution of continuous variables is to divide the set of possible values into intervals and count the number of values in each interval.

When plotting a histogram in pandas, you can set the number of intervals (bins) and set their boundaries explicitly:

data.hist( bins = [value1, value2, value.... valueN])

To overcome the difficulties of creating a histogram for a continuous variable, we can use a slightly different technique that represents frequency not as the height of a column, but as its area (the length of the interval times the height of the column). This area is the frequency of the continuous variable, and the height of the column is the frequency density. A histogram that uses frequency density is called a density histogram.  To estimate how many values fall in a particular interval, take two values and find the total area of the density histograms between them. The number you get will be an estimate of the number of values in that interval. 

Q:

Data with negative skew (skewed to the left) has ...


A:

Data with negative skew (skewed to the left) has a mean that is less than the median. The data will have more values below the mean than above it.

Q:

How do you call the std func in python. What library do you use?

A:

standard_deviation = np.std(dataset)

Q:

Data with positive skew (skewed to the right) has... 


A:

Data with positive skew (skewed to the right) has a mean that is greater than the median. The data will have more values greater than the mean than below it.

Mehr Karteikarten anzeigen
Q:

Find dispersion of data

A:

import numpy as np 

np.var(data) 

Q:

What is Frequency density ?

A:

Frequency density — a value equal to the height of a histogram column whose area reflects the relative frequency of a continuous variable.

Q:

Define a discrete variable.

A:

 A  discrete variable is any variable that is not continuous on any range (for example a variable that takes the integer values from 0 to 100). 

Q:

What is a Density histogram 

A:

Density histogram — a histogram that uses frequency density. 

Q:

What is μ?

A:

The mean, often denoted by the Greek letter mu, μ. 

Q:

Build histograms of data with set interval boundaries 16,18,20,58,100


A:

data.hist(bins=[16,18,20,58,100 ]) 

Q:

If mean > median 

is it a positive or negative skew?

A:

if the mean is greater than the median, then the data has positive skew. Similarly, if the mean is less than the median, then the data has negative skew. 

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