5 - 4 + 7x + 1 = 7x + (5 - 4 + 1) = 7x + 2
NO SOLUTIONS:
<em>5 - 4 + 7x + 1 = 7x + a </em><em>(a - any real number, except 2)</em>
ONE SOLUTION:
<em>5 - 4 + 7x + 1 = bx + c </em><em>(b - any real number, except 7, c - any real number)</em>
INFINITELY MANY SOLUTIONS:
<em>5 - 4 + 7x + 1 = 7x + 2</em>
Examples:
2x + 3 = 2x + 5 <em>subtract 2x from both sides</em>
3 = 5 FALSE <em>(NO SOLUTIONS)</em>
2x + 3 = x - 4 <em>subtract x from both sides</em>
x + 3 = -4 <em>subtract 3 from both sides</em>
x = -7 (<em>ONE SOLUTION)</em>
2x + 3 = 2x + 3 <em>subtract 2x from both sides</em>
3 = 3 TRUE <em>(INFINITELY MANY SOLUTIONS)</em>
Ok so f(x) and g(x) are the same thing as y. this means that you can solve these equations the same as any other multi-step equation. the only difference is if they have a number other than x in the parentheses. if they have a number you will just replace the x’s in the equation with the number in the parentheses.
example:
regular equation equation with f(4)
f(x)=2x+1 f(4)=2(4)+1
make sure to use pemdas :)
Yeah i agree with them y=10
J= 25+3
j-25=3
I'm pretty sure either would work
Answer:
Statistical error is the difference between the estimated or approximated value and the true value.
<u>Two Possible Types of Statistical Error</u>
Type I Errors occur when we reject a null hypothesis that is actually true; the probability of this occurring is denoted by alpha (a).
Type II Errors are when we accept a null hypothesis that is actually false; its probability is called beta (b).
<u>Example </u>
You test whether a new drug intervention can alleviate symptoms of an autoimmune disease.
A Type I error happens when you get false positive results: you conclude that the drug intervention improved symptoms when it actually didn’t. These improvements could have arisen from other random factors or measurement errors.
A Type II error happens when you get false negative results: you conclude that the drug intervention didn’t improve symptoms when it actually did. Your study may have missed key indicators of improvements or attributed any improvements to other factors instead.
