General 3 Flashcards
Marginal effect =
b1*2b2
marginal effect or Top of sahmi or maximum point
B1/ 2b2.
A logarithmic scale
is nearer to linear
T statistics in regression analysis:
b1 – B1/sb1 in fact: b1/sb1
Sb1=
the square root of MS error / SSx
Interaction term,
The product of two variables in regression x1 and x2 incorporate the effect of the x2 variable when the variable x1 affects the relationship.
In regression with multiple dummy variables, what is the most probable relationship?
Parabolic
Dummy variable TRAP,
Is it impossible to model to tease out whether the effect comes from x1 or come from x2?
Always dummy variables can be
divided into subdivisions that could impact the significance of other variables.
If the dummy variable equal to 1 =
there is x amount more or less NOT one-unit decrease or increase and etc. in fact the dummy variable coefficient moves whole of regression line up or done according to y-axis NOT slope!
Dummy variable use when
the x variable consists of multiple levels or is represent a nonlinear relationship,
To move the parabola from the y-axis because of the y=x2 draw at the center of the y-axis!
So add another repetitive without power variable to move the parabola from the y-axis. So the parabola base not on the y axis in the regression equation.
یک تابع گویا در ریاضیات، تابعی است که به صورت کسر قابل بیان است
برای نشان دادن رابطه بین دو متغیر رگرسیون وقتی یکی از ضرایب کسری یا معکوس شده باشد. Asymptotic nam dard ke bayangare shekle monhani ast
The overall significance of the regression equation NOT occured
slope parameters are jointly zero.
Overall significance of regression model determines
f-test and individual significance determined by t-test.
F-statistics more than f-critical value:
Reject the null hypothesis.
K is
and n-k-1 is
the horizontal top at the table
the vertical line of the f table.
For the f test, we need
two degrees of freedom the first is k and the second is n-k-1.
F critical value:
From the table with two degrees of freedom
F-statistics 1 =
MSR / MSE
F-statistics2 =
(R^2⁄(k-1))⁄((1-R^2)⁄(n-k))
95 percent confidence interval=
95 percent of the area under the curve is between x and y.
Using a 5 percent probability means that
There is a 5 percent probability of incorrectly rejecting the null hypothesis and it is acceptable.
P-value ˂ 0.05
reject the null hypothesis
P- value ˃ 0.05
not reject the null hypothesis.
P-value ˃ Alpha,
the difference between groups not statistically significant.
P-value ˂ Alpha
Statistically significantly different between groups. Less than 5 percent prob that null hypothesis is true.
Whether a p-value is low or high?
Determined by Level of significance or alpha.
P value= 0.001 (under 0.05) interpretation=
there is a probability of 0.001 that you mistakenly reject the null hypothesis.
Low p-value:
reject the null hypothesis,
high p-value:
fail to reject null.
The t-test just
not consider the mean but also consider the standard deviation in analysis the difference between two sets of data.
One-tailed test:
1- detect the effect in one direction 2- will test either the mean is greater than x or the mean less than x but NOT both of them. Ex, in close-ended questions (Yes/ No). Are boys significantly taller than girls or are girls significantly taller than boys?
Two-tailed test:
test both when mean is significantly greater than x and if the mean significantly greater lower than x. ex. Open-ended questions: is there a significant difference between the height of boys and the height of girls? So girls or boys could be taller!
Every spread of the data until two standard deviations
consider normal.
Anything in the 95 percent of data under the normal distribution
considers as normal.
the bigger sample
the close we get the actual numbers.
After 30 degrees of freedom,
the t-statistics table becomes relatively the same as z-statistics (normal distribution), so for more than 50 observations, we use z table instead of the t table.
The purpose of the t-student test:
is there a significant difference between two sets of data?
In t statistic test:
The t=b1/Sb1
One tail test
Hypothesis testing is run to determine whether a claim is true or not, given a population parameter. A test that is conducted to show whether the mean of the sample is significantly greater than and significantly less than the mean of a population is considered a two-tailed test.
Two tail test
A Two-Tailed Hypothesis is used in statistical testing to determine the relationship between a sample and a distribution. In statistics, you compare a sample (Example: one class of high school seniors SAT scores) to a larger set of numbers, or a distribution (the SAT scores for all US high school seniors).
