No. The mean is the expected value of the random variable but you can also have expected values of functions of the random variable. If you define X as the random variable representing the result of a single throw of a fair die, the expected value of X is 3.5, the mean of the probability distribution of X. However, you play a game where you pay someone a certain amount of money for each throw of the die and the other person pays you your "winnings" which depend on the outcome of the throw. The variable, "your winnings", will also have an expected value. As will your opponent's winnings.
Minimum Expected Regret ( EVPI = Expected Regret of the best solution)
Choose some values for x. Then calculate the corresponding values of y using the formula. Put these values in a table.Choose some values for x. Then calculate the corresponding values of y using the formula. Put these values in a table.Choose some values for x. Then calculate the corresponding values of y using the formula. Put these values in a table.Choose some values for x. Then calculate the corresponding values of y using the formula. Put these values in a table.
No. The expected value is the mean!
The expected value is the average of a probability distribution. It is the value that can be expected to occur on the average, in the long run.
The answer to both questions is yes.
as expected by the proper values there is an increase in technology by an increase of money and research
A residual is defined in the context of some "expected" value. There is no information in the question regarding expected values.
The chi-squared test is used to compare the observed results with the expected results. If expected and observed values are equal then chi-squared will be equal to zero. If chi-squared is equal to zero or very small, then the expected and observed values are close. Calculating the chi-squared value allows one to determine if there is a statistical significance between the observed and expected values. The formula for chi-squared is: X^2 = sum((observed - expected)^2 / expected) Using the degrees of freedom, use a table to determine the critical value. If X^2 > critical value, then there is a statistically significant difference between the observed and expected values. If X^2 < critical value, there there is no statistically significant difference between the observed and expected values.
an ionic bond
an ionic bond
You seem to be referring to the Pearson chi-square test-of-fit statistic. To do this you need not only the observed values in a frequency table (which you have) but the expected (or theoretical) values for that table.In practical situations the expected values are obtained by making some educated guess about what distribution the observed values came from, estimating the parameters of that distribution and then using the estimated distribution to obtain the required expected values to calculate the chi-square.In short, you need more information.
A lemon is acidic so a low pH is expected. The pH of lemon juice is about 2.0, which means this fruit is highly acidic.
It means that the observed values in the experiment all exactly match the expected values. That is unlikely, unless the experiment was "fixed".
A good measure of dispersion is one such that the a goodness-of-fit test shows that the observed values agree well with the expected values.
The expected value is the long-run average value of repetitions of the experiment it represents.