Probability and Statistics: A Comprehensive Overview
1.V
The binomial model is characterized by dichotomous. (There – FAILURE)
2.F
The normal curve is asymmetrical with respect to the mean. (DEPENDS definition)
3.F
Probability theory works with deterministic experiments.
(RAND PROBABILISTIC).
4x
Probability theory allows to get to build a model of the random experiment.
5.F
The route of the standard model is defined on positive real numbers. (Negative-positive)
6.V
Decisions based on probability theory are positive.
7.F
A random experiment is the process of gathering information for an event that shows a result when repeated several times. (Samples at random)
Probability theory 8.V delivery procedures for calculating the results of a randomized trial.
9.F in a randomized experiment can define a set of possible outcomes.
10.F
The set of possible outcomes of the random experiment recognized by random experiment.
11.F binomial model events are dependent (INDEPENDENT).
12.V
If the experiment is to throw three coins, the number of possible outcomes will be 8.
13.V
An event is a subset and a sample space.
14.V the empty set is known as an event emptied.
15.V
If the probability of an event approaches one then the event is very likely to happen.
16.F
The sample space is known as an event likely.
17.V
A probability can be defined as: frecuentita and subjective.
18.V
A Bayesian probability is the degree of certainty people have about an event.
19.F
The opposite is the event that consists of all elements
20.V placed with the sample space.
21.F
A sample space is finite and countable if it has a finite number of terms and these and these belong to the real numbers (NOT JUST THE ACTUAL)
22.V
The probability of the sample space is equal to one half (or failure).
23.F
The probability of a subset is the relative size in total.
The probability of n 24.V subset is the relative size in total.
25.V
Let A c E such that P (A) = 1-P (A)
26.F
Let A1, A2 c E such that any events: P (A1-A2) = P (A1UA2)
27.V
The probability that event A occurs is calculated by P (A) = # A / # E
28.V
The union is the event which consists of the experimental results that are in A or B or both.
29.V
To calculate a conditional probability is to calculate the intersection between two events.
30.V
In the Gaussian model is bell-shaped curve.
31.F subjective probability of an event is the relative frequency of times that the event would happen to run an experiment again and again.
32.F
A system is comprehensive and inclusive if the union of events is a sample space and their interaction is different vacuum
33.V
Two events are independent if one occurs, it adds information on the other.
34.V
Bayes theorem calculates a conditional probability of an event Ai (i = 1,2, …, n) of the partition of the sample space conditional on event B.
35.F
The specificity is determined by the probabilities of the true positives.
36.V
The prevalence is the percentage of the population who has an illness.
37.V
P (Ill / +) = positive predictive index.
38.V
A random variable is a function which assigns each event a number.
39.F random variables can be described as discrete and discontinuous.
40.V
A density function is a nonnegative function of area equal to one.
41.F
The incidence is the percentage of cases of disease present in a population.
42.V
In the probability density functions of an interval describes a certain area.
43.F
The expected value equals the median.
44.F
The sensitivity is determined by the probabilities of the true negatives.
45f
The parameters of a normal model are: the mean and proportion.
46.V
In a normal average model factor is the location of the curve.
47.V
Kart Gauss determined the normal pattern through stellar observations.
48.F
P (Ill / -) = negative predictive index.
49.V normal model delivers the standard deviation curve shape.
50.F
The success of this intersection is composed of the elements that are in A or B.
1.V
SPSS normality test indicates that Ho: variable is normal and that H1: the variable is not normal.
2x the normal model is determined by the mean and desviació? Nt? Pike.
3.V
In Gauss is known that between half and one desviació? Nt? Pike likely have about 68%.
4x
The funci? N normal density is sim? Trica.
5x
To estimate the pair? Meters from the linear regression model? N using the so called for estimates? Nm? Nimo picture? Tica errors.
The 6.V normal with mean 0 and desviació? Nt? Pica 1 is known as standard normal.
7.V
For normal variable X, the Interpretaci? N is: Assign to every value of N (and, or) a value of N (0,1) that leaves exactly the same probability below.
8.V
The average in the normal model is a translational factor? N.
The desviació 9.V?
Nt? Pike in the normal pattern determines the shape of the curve.
10.V
If P (Z <1.85) = 0.068 then P (Z> 1.85) = 0.032.
11. The probability P (Z <0) = 0.25
12. Although not a random variable has a distribution? N normal, certain states? Stico / estimators calculated on large random samples, if possess? N a distribution? N normal.
13. The desviació? Nt? Pica always be average? Equivalent to desviació? Nt? Pica normal variable.
14. The average of a random sample that comes in a population? N be normal anyway? Normal.
15.F With tipificaci? No one can compare different measurements of normal models.
16.V If N> (greater) 20, the average be? Normal.
17.F
The Chi-square model is sim? Trich.
18.F
Student T model is sim? Igniter on average.
19.V
Gaussian model appears in the appearances of measurement errors.
20. If n> 30 and p sin? Or (np> 5), n large then the model can approximate the Poisson curve.
21.V
F snededor model has two pair? Meters.
The population 22.V?
N ideal for investigation is called poblaci? No goal.
23.V
Chi-square test applied to verify the independence or insert variables into scales raz? N.
24.F
The group that we can actually study called poblaci? No target.
25.V sampling probabil? Sticos know the probability that an individual is elected to the sample.
Sampling 26.F probabil not? Stico no bias.
27. The t? Cnicas inference estad? Stica assume that the sample was selected using more
28.V
To avoid such biases we used the t? Cnicas random response.
29.V
The cluster sampling is applied when it’s hard to have a list of all individuals who are part of the poblaci? No study.
30.F
An estimator is a sum num? Rich calculated on a sample and it is a good representational? No one state? Stico.
31.F
The desviació? Nt? Pike from the sample mean is o / n.
32. The bias due to different systems? Cies between poblaci? No objective and poblaci? No study is called The selection bias? N.
33. The Estimate? No confidence interval given a set of estimates and probability of error.
34. The inference estad? Stica is the set of m? All of which lead to character? Sticas from a sample probability? Stica.
35. A hip? Thesis estad? Stica is a procedure to establish a decisive? N with respect to random variables est? N present a probability model.
36.V
The covariance measures the strength of relaci? N.
37.V
The Pearson r coefficient est? Between 1 and -1.
38.V
The coefficient r is dimensionless.
The 39.V an? Analysis of regression? N used to predict the relaci? N of the dependent variable in funci? No independent variable.
40.F
In the hip? Thesis H1: the data can refute it.
41.F
In the hip? Thesis H0: no duty? To be accepted with strong evidence in favor.
42.V
The confidence level is 90%, then the probability of error is 0.10.
43.V
The coefficient r square interpretation, the percentage of variability of the independent variable.
The graphic Dispersió 44.V?
N, is a graph of Gauss, which measure the tendency of the data.
45f
If p> H1 Alpha is rejected.
46.V
The contrast is not significant, when p> alpha.
47.V
The error type two, says, H0 is accepted as this is false
48. The Mann Whitney test is a test to compare the means of two related samples.
49.F
The p-value is known before the experiment.
50. The Wilxcon test, a test is not param? Trica to get the means of two related samples.