A square wave has the highest RMS value. RMS value is simply root-mean-square, and since the square wave spends all of its time at one or the other peak value, then the RMS value is simply the peak value.
If you want to quantify the RMS value of other waveforms, then you need to take the RMS of a series of equally spaced samples. You can use calculus to do this, or, for certain waveforms, you can use Cartwright, Kenneth V. 2007.
In summary, the RMS value of a square wave of peak value a is a; the RMS value of a sine wave of peak value a is a divided by square root of 2; and the RMS value of a sawtooth wave of peak value a is a divided by cube root of 3; so, in order of decreasing RMS value, you have the square wave, the sine wave, and the sawtooth wave.
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okay, where's the "given waveform"?
The constant R in the sawtooth wave formula affects the slope of the rising edge of the wave. A higher R value will result in a steeper rising edge, while a lower R value will create a more gradual slope.
0.0625 square units
Not enough information has been given to determine the value of x
The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.
2½ ie 2 to the power of one-half. The power "one-half" applied to any value signifies that value's square root. Do not confuse with the power "-1" which indicates the reciprocal of the given value.
The answer depends on the units used for the given value. Without that information the question cannot be answered.
R2 refers to the fraction of variance. it is the square of the correlation coefficient between two dependent variables. It is a statistical term that tells us how good one variable is at predicting another. If R2 is 1.0, then given the value of one variable you can perfectly predict the value of the other variable. If R2 is 0.0, then knowing either variable does not help you predict the other variable. In turn, the higher the R2 value the more correlation there is between the two variables.
A square root is multiplied by itself to get a given whole number.
the data value that is much higher or lower than the other data given is called an outlier
540
Caviar, because it has a higher market value than hamburger. if you follow the definition (GDP: market value of all final goods and services produced within a country in a given period of time) the market value of caviar is substantially higher than the market value of hamburger.