Statistics Final Exam

Front 1

the researcher merely observes what ishappening or what has happened in the past and tries to draw conclusions basedon these observations

Back 1

Observational Study

Front 2

the researcher manipulated one of thevariables and tries to determine how the manipulation influences othervariables

Back 2

Experimental Study

Front 3

a characteristic or measure obtained by using the data values from a sample.

Back 3

Statistic

Front 4

a characteristic or measure obtained by using all the data values from a specific population

Back 4

Parameter

Front 5

consists of all subjects being studied

Back 5

Population

Front 6

a group of subjects from the population

Back 6

Sample

Front 7

the organization of raw data in table form, using classes and frequencies

Back 7

Frequency Distribution

Front 8

raw data values are placed into a quantitative or qualitative category called a class

Back 8

Class limits

Front 9

the largest and smallest value that can be included in that class

Back 9

Upper and lower class limits

Front 10

the number of values in each class

Back 10

Frequency

Front 11

numbers used to separate the classes so that there are no gaps in the frequency distribution

Back 11

Class boundaries

Front 12

is found by subtracting the lower (or upper) class limit of one class from the lower (or upper) class limit of the next class

Back 12

Class width

Front 13

Class midpoint

Back 13

Xm= (Lower Limit+Upper limit)/2

Front 14

shows the number of data values that are less than or equal to a specific value ; found by adding the frequencies of the classes less than or equal to the upper class boundary of a specific class ; shows the number of data values that are less than or equal to a specific value ; found by adding the frequencies of the classes less than or equal to the upper class boundary of a specific class D

Back 14

Cumulative frequency distribution

Front 15

when the range of data values is very small a frequency distribution can be constructed using single data values for each class

Back 15

Ungrouped Frequency distribution

Front 16

a graph that displays the data by using contiguous vertical bars (unless the frequency of the class is 0) of various heights to represent the frequency of the classes

Back 16

Histogram

Front 17

a graph that displays the data by using lines that connect points plotted for the frequencies at the midpoints of the classes. The frequencies are represented in the heights of the points

Back 17

frequency polygon

Front 18

(aka the cumulative frequency graph) is a graph that represents the cumulative frequencies of the classes in a frequency distribution

Back 18

Ogive

Front 19

Variance Equation

Back 19

σ^2=(Σ(X−μ))/N

Front 20

Standard deviation equation

Back 20

σ=√((Σ(X−μ))/N)

Front 21

Q2=

Back 21

Median

Front 22

Q1=

Back 22

median for all values that fall below the median

Front 23

Q3=

Back 23

median for all the data values that are above the median

Front 24

IQR=

Back 24

Q3-Q1

Front 25

Back 25

Box plot

Front 26

a graph of a data set obtained by drawing a horizontal line from the minimum data value to Q1 , drawing a horizontal line from Q3 to the maximum data value, and drawing a box whose vertical sides pass through Q1 and Q3 with a vertical line inside the box passing through the median

Back 26

box plot

Front 27

cannot occur at the same time (have no outcomes in common)

Back 27

Mutually Exclusive events

Front 28

The chance of an event occurring

Back 28

Probability

Front 29

the probability of event E is a number (either a fraction or a decimal) between and including 0 and 1. This is denoted by 0≤P(E) ≤ 1

Back 29

Probability rule 1

Front 30

If an event E can occur (i.e., the event contains no members in the same sample space), its probability is 0.

Back 30

Probability Rule 2

Front 31

If an event E is certain, then the probability of E is 1.

Back 31

Probability Rule 3

Front 32

The Sum of the Probabilities of all the outcomes in the sample space is 1

Back 32

Probability Rule 4

Front 33

Ρ(Ē)=1−P(E)

P(E)=1−P(Ē)

P(E)+P(Ē)=1

Back 33

Probability rule for complementary events

Front 34

Formula for emprical probability

Back 34

P(E)= (frequency for the class)/(total frequencies in the distribution)=f/n� distribution)=f/n

Front 35

Properties of a Binomial experiment

Back 35

Fixed number of trials

Each trial can only have two outcomes or outcomes that can be reduced to two outcomes. These can be considered either a success or a fail

The outcomes of each trial must be independent of one another

The probability of a success must remain the same for each trial

Front 36

the outcomes of a binomial experiment

Back 36

Binomial distribution

Front 37

a continuous, symmetric, bell-shaped distribution of a variable

Back 37

Normal Distribution

Front 38

Properties of Normal Distribution

Back 38

Bell shaped

Mean, median, and mode are located at the center

Unimodal

The curve is symmetric about the mean

Front 39

computed from the same sample data measures the strength and direction of a linear relationship between two quantitative variables. The symbol for the population correlation coefficient is ρ (Greek letter rh�)

Back 39

Correlation Coefficient

Front 40

Equation for Correlation Coefficient

Back 40

r=(n(Σxy)−(Σx)(Σy))/√([n(Σx^2 )−(Σx)^2 ][n(Σy^2 )−(Σy)^2])

Front 41

a statistical test for the mean of a population. It can be used when n≥30 or when the population is normally distributed and σ is known

Back 41

Z-test