// Numbas version: finer_feedback_settings {"name": "Differentiation: Chain Rule - Hyp Log", "extensions": [], "custom_part_types": [], "resources": [], "navigation": {"allowregen": true, "showfrontpage": false, "preventleave": false, "typeendtoleave": false}, "question_groups": [{"pickingStrategy": "all-ordered", "questions": [{"advice": "\n\t \n\t \n\t
Here is a table of the derivatives of some of the hyperbolic functions:
\n\t \n\t \n\t \n\t$f(x)$ | $\\displaystyle{\\frac{df}{dx}}$ |
---|---|
$\\sinh(bx)$ | $b\\cosh(bx)$ |
$\\cosh(bx)$ | $b\\sinh(bx)$ |
$\\tanh(bx)$ | $\\simplify{b*sech(bx)^2}$ |
a)
\n\t \n\t \n\t \n\t$f(x)=\\simplify[std]{ x ^ {n} * sinh({a1} * x + {b1})}$
\n\t \n\t \n\t \n\tUse the product rule to obtain:
\\[\\frac{df}{dx} = \\simplify[std]{{n} * (x ^ {(n -1)}) * sinh({a1} * x + {b1}) + {a1} * (x ^ {n}) * Cosh({a1} * x + {b1})}\\]
b)
\n\t \n\t \n\t \n\t$f(x)=\\tanh(\\simplify[std]{{a}x+{b}})$
\n\t \n\t \n\t \n\tUsing the table above we get:
\\[\\frac{df}{dx} = \\simplify[std]{{a}*sech({a}x+{b})^2}\\]
c)
\n\t \n\t \n\t \n\t$f(x)=\\ln(\\cosh(\\simplify[std]{{a2}x+{b2}}))$
\n\t \n\t \n\t \n\tUsing the chain rule we find:
\n\t \n\t \n\t \n\t\\[\\frac{df}{dx} = \\simplify[std]{{a2} * tanh({a2} * x + {b2})}\\]
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\n[NOTE: To input the square of a function such as $\\sinh(x)$ you have to input it as (sinh(x))^2, similarly for the other hyperbolic functions.
\n", "functions": {}, "rulesets": {"std": ["all", "fractionNumbers", "!collectNumbers", "!noLeadingMinus"]}, "parts": [{"showFeedbackIcon": true, "prompt": "$f(x)=\\tanh(\\simplify[std]{{a}x+{b}})$
\n$\\displaystyle{f'(x)=\\;\\;}$[[0]]
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\n$\\displaystyle{\\frac{dy}{dx}=\\;\\;}$[[0]]
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\n\t\t\t$\\displaystyle{\\frac{df}{dx}=\\;\\;}$[[0]]
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