A keypad at the entrance of a building has 10 buttons labeled 0 through 9. What is the probability of a person correctly guessing a 9-digit entry code if they know that no digits repeat?
Answer:
the probability of a person correctly guessing a 9-digit entry code if they know that no digits repeat is 0.1
Step-by-step explanation:
We know that probability= number of required outcomes /number of all possible outcome.
From the given information;
the number of required outcome is guessing a 9-digit = 1 outcome
the number of all possible outcome = ¹⁰C₉ since there are 10 numbers and 9 number are to be selected.
Since there are only 9-digit that opens the lock;
the probability of a person correctly guessing a 9-digit entry code is
P = 0.1
Answer:
It should be -5/2 for the points (-6,8) (-16,33)
Step-by-step explanation:
Answer:
1. Discrete-time signals are represented mathematically as sequences of numbers. A sequence of numbers x, in which the nth number in the sequence is denoted x[n],
1 is
formally written as
x = {x[n]}, −∞ <n< ∞, (2.1)
2. where n is an integer. In a practical setting, such sequences can often arise from periodic
sampling of an analog (i.e., continuous-time) signal xa(t). In that case, the numeric value
of the nth number in the sequence is equal to the value of the analog signal, xa(t), at
time nT : i.e.,
x[n] = xa(nT ), −∞ <n< ∞. (2.2)
Step-by-step explanation:
Answer:
D. method of least squares
Step-by-step explanation:
The Least Squares Method (LSM) is a mathematical method used to solve various problems, based on minimizing the sum of the squared deviations of some functions from the desired variables. It can be used to “solve” over-determined systems of equations (when the number of equations exceeds the number of unknowns), to find a solution in the case of ordinary (not redefined) linear or nonlinear systems of equations, to approximate the point values of a function. OLS is one of the basic regression analysis methods for estimating the unknown parameters of regression models from sample data.
Correlation analysis is a statistical method used to assess the strength of the relationship between two quantitative variables. A high correlation means that two or more variables have a strong relationship with each other, while a weak correlation means that the variables are hardly related. In other words, it is a process of studying the strength of this relationship with available statistics.
Analysis of Variance (or ANOVA) is a collection of statistical models used to analyze group averages and related processes (such as intra- and inter-group variation) in statistical science. When using Variance Analysis, the observed variance of a specified variable is divided into the variance component that can be based on different sources of change. In its simplest form, "Analysis of Variance" is a inferential statistical test to test whether the averages of several groups are equal or not, and this test generalizes the t-test test for two-groups to multiple-groups. If multiple two-sample-t-tests are desired for multivariate analysis, it is clear that this results in increased probability of type I error. Therefore, the variance analysis would be more useful to compare the statistical significance of three or more means (for groups or for variables) with the test.
Regression analysis is an analysis method used to measure the relationship between two or more variables. If analysis is performed using a single variable, it is called univariate regression, and if more than one variable is used, it is called multivariate regression analysis. With the regression analysis, the existence of the relationship between the variables, if there is a relationship between the strength of the information can be obtained. The logic here is that the variable to the left of the equation is affected by the variables to the right. The variables on the right are not affected by other variables. Not being influenced here means that when we put these variables into a linear equation in mathematical sense, it has an effect. Multiple linearity, sequential dependency problems are not meant.
