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Business Statistics (BUSI.2305) Summer 1 2022 - Coggle Diagram
Business Statistics (BUSI.2305)
Summer 1 2022
Module 1: Data Collection
Module 2: Data Representation
Module 3: Data Analysis
Confidence Intervals
Point Estimate is the use of a sample data to calculate a single value or approximate value which is serve as the "best guess" of an unknown parameter
JH
EBM
EBM stands for Error Bound Mean. EDM provides the boundary of the interval derived from the standard error of the sampling distribution. It's driven by the estimated mean, sample mean, plus or minus something. That something is the error bound.
(
BL
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To find the error bound find the difference of the upper bound of the interval and the mean. If you do not know the sample mean, you can find the error bound by calculating half the difference of the upper and lower bounds. (ES)
Empirical Rule
EBP
Confidence Interval: this represents an unknown population parameter by creating an interval estimate, but the confidence interval estimate can be due so different factors. for instance, the confidence level desired, all the information about the distribution, or what is the size of the population parameter and sample size. (ZM)
Degrees of Freedom
Confidence Level-
Hypothesis Testing
Type II Error
A type II error is a false negative conclusion. In statistics the hypothesis we come up with many not always be correct. When the test in your experiment does not work then that would be a type II error:meaning you missed a change in the experiment somewhere.
D.M
Hypothesis Testing
Statistics is base on testing hypothesis, so hypothesis testing is a method to represent and know if the data you have already, is sufficient to support and represent the actual hypothesis. It can confirm if your hypothesis is right or wrong.
(AS)
Type I Error
Type I Error means that the null hypothesis was rejected when it is actually true. The results conclude they are statistically significant and in reality the results are purely because of unrelated factors or simply by chance. An example of a Type I Error would be sending an innocent person to jail.
E.R.
t-Distribution
The bell-shaped T-distribution is a sort of probability distribution that resembles the normal distribution but has longer tails. T distributions have broader tails than normal distributions because they are more likely to contain extreme values.
(SL)
Alternative Hypothesis
The alternative hypothesis is a statement used in statistical inference experiment. It is contradictory to the null hypothesis and it is depicted b the symbol H1 and is also on the idea of it being the alternative method or hypothesis from the null. In hypothesis testing, an alternative theory is a statement which a researcher is testing.
K.C
test statistic
The test statistic describes how far your data is from your hypothesis. It is calculated from a sample data and to is used in a hypothesis test.
(PE)
Null Hypothesis
When one is testing a null Hypothesis It is often referred to as (H0) this means that this type of hypothesis is a starting point in which two possible solutions might have the same outcome . this Hypothesis is also used for inferencing the possible solutions to the experimental data that is being calculated.
J.P
p-value
The p-value is another alternative or approach when talking about probability. This term, means the number describing how likely a null hypothesis would have actually occurred while under a statistical test.
(EC)
Probability
Definitions
Event
Independent Event
The occurence of one event has no effect on the probability of the occurrence of another event.
Events A and B are independent if one of the following is true:
P(A|B)=P(A)
P(B|A)=P(B)
3.P(A n B)=P(A)P(B)
KH
Sample Space
the sample space can be said to be all the possible results to be had based on an experiment that will be performed randomly
(MA)
Mutually Exclusive Event: This means that two events can not happen at the same time, they are both separate from each other and they do not share any outcomes.
P (A and b) = 0 (ZM)
Outcome
An outcome is that which is obtained from carrying out an experiment or trial and finishing it. If the experiment is carried out many times, it may show different results.
(MA)
Union
The U event - an outcome is in the event A U B if the outcome is in. A or in B or both in A and B. (R.F)
Chance
Intersection
Mutiplication Rule
Addition Rule: describes two formulas, one for the probability for either of two mutually exclusive events happening:
P(Y or Z) = P(Y) + P(Z)
and the other for the probability of two non-mutually exclusive events happening:
P(Y or Z) = P(Y) + P(Z) - P(Y and Z)
NP
Experiment
Conditional Probability: p(A|B) is the probability of event A occurring, given that event B occurs.
JR
Module 4: Data Interpretation